SARIMA_2020_10_07.ipynb

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  "metadata": {
    "colab": {
      "name": "SARIMA_2020-10-07",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNet2l4mlZJpiwXL/JJ8wID",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ejihoon6065/Project_TurnAround/blob/master/SARIMA_2020_10_07.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1aIshWIMpjh",
        "outputId": "ed568a1d-8e09-4055-c859-b6a4b60b2443",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import pandas as pd\n",
        "import csv\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "#sns.set() # setting seaborn default for plots\n",
        "import matplotlib.font_manager as fm\n",
        "import copy\n",
        "# 그래프에서 마이너스 폰트 깨지는 문제에 대한 대처\n",
        "mpl.rcParams['axes.unicode_minus'] = False\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "from scipy import stats  # Box-Cox 검정\n",
        "import statsmodels.api as sm  # qqplot\n",
        "from statsmodels.tsa.stattools import adfuller    # adfuller : ADF그래프 그려주는 함수\n",
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "from pandas.plotting import register_matplotlib_converters\n",
        "from statsmodels.stats.diagnostic import acorr_ljungbox # Ljung-Box 검정\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import itertools\n",
        "\n",
        "\n",
        "# 돌리기전에, https://colab.research.google.com/drive/1oBXX3k3OOjdivwlDhpLmAqfq5B5dzFR1#scrollTo=AbTlhWommsXE 에 있는 코드도 참고 \n",
        "# https://towardsdatascience.com/stock-market-analysis-using-arima-8731ded2447a 참고"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lteXXeA2Mr1v",
        "outputId": "1b5479fd-11db-44f9-fe5b-0c270413247f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install -U pykrx  # KRX데이터를 크롤링하기 위한 라이브러리설치"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pykrx\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c9/12/70cc31494c9e727fa216103b674d231124fabdac6ee8a4d8f43059afbc36/pykrx-0.1.41-py3-none-any.whl (40kB)\n",
            "\u001b[K     |████████████████████████████████| 40kB 1.8MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from pykrx) (1.18.5)\n",
            "Requirement already satisfied, skipping upgrade: pandas in /usr/local/lib/python3.6/dist-packages (from pykrx) (1.1.2)\n",
            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from pykrx) (2.23.0)\n",
            "Collecting datetime\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/73/22/a5297f3a1f92468cc737f8ce7ba6e5f245fcfafeae810ba37bd1039ea01c/DateTime-4.3-py2.py3-none-any.whl (60kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 3.3MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: xlrd in /usr/local/lib/python3.6/dist-packages (from pykrx) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->pykrx) (2018.9)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->pykrx) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->pykrx) (2.10)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->pykrx) (2020.6.20)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->pykrx) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->pykrx) (3.0.4)\n",
            "Collecting zope.interface\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c5/89/1eb9dbb9e24f5e2c29ab1a88097b2f1333858aac3cd3cccc6c4c1c8ad867/zope.interface-5.1.2-cp36-cp36m-manylinux2010_x86_64.whl (236kB)\n",
            "\u001b[K     |████████████████████████████████| 245kB 20.8MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas->pykrx) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from zope.interface->datetime->pykrx) (50.3.0)\n",
            "Installing collected packages: zope.interface, datetime, pykrx\n",
            "Successfully installed datetime-4.3 pykrx-0.1.41 zope.interface-5.1.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1e_jLtaMufy",
        "outputId": "932e4ad8-cfb5-43d4-daa1-ed14f4d8c0b0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0ym91avMwJu"
      },
      "source": [
        "from datetime import datetime,timedelta\n",
        "\n",
        "today=datetime.today().strftime(\"%Y%m%d\")                      # 현재날짜\n",
        "year1=(datetime.today()-timedelta(365)).strftime('%Y%m%d')     # 현재날짜로부터 1년전 날짜"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKKvtxFjyc5G",
        "outputId": "e5c6480f-d39d-4f92-fae2-e2b4636b0627",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(today,year1)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "20201007 20191008\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQ5oe-HmM0qn",
        "outputId": "1b8e9ae5-8984-4ae5-d372-d94735f4239a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        }
      },
      "source": [
        "# 주가 크롤링\n",
        "from pykrx import stock\n",
        "df = stock.get_market_ohlcv_by_date(year1, today, \"122870\")  # (시작날짜,종료날짜,종목번호)\n",
        "df"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>와이지엔터테인먼트</th>\n",
              "      <th>시가</th>\n",
              "      <th>고가</th>\n",
              "      <th>저가</th>\n",
              "      <th>종가</th>\n",
              "      <th>거래량</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>날짜</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-10-08</th>\n",
              "      <td>23400</td>\n",
              "      <td>23900</td>\n",
              "      <td>22900</td>\n",
              "      <td>23800</td>\n",
              "      <td>148848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-10</th>\n",
              "      <td>23550</td>\n",
              "      <td>23700</td>\n",
              "      <td>23000</td>\n",
              "      <td>23550</td>\n",
              "      <td>150525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-11</th>\n",
              "      <td>23800</td>\n",
              "      <td>25200</td>\n",
              "      <td>23650</td>\n",
              "      <td>23950</td>\n",
              "      <td>474910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-14</th>\n",
              "      <td>24350</td>\n",
              "      <td>25850</td>\n",
              "      <td>24150</td>\n",
              "      <td>25650</td>\n",
              "      <td>558471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-15</th>\n",
              "      <td>25600</td>\n",
              "      <td>26700</td>\n",
              "      <td>25350</td>\n",
              "      <td>26000</td>\n",
              "      <td>425303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-28</th>\n",
              "      <td>52300</td>\n",
              "      <td>57500</td>\n",
              "      <td>51800</td>\n",
              "      <td>57000</td>\n",
              "      <td>1067907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-29</th>\n",
              "      <td>57700</td>\n",
              "      <td>58000</td>\n",
              "      <td>54900</td>\n",
              "      <td>58000</td>\n",
              "      <td>758762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-05</th>\n",
              "      <td>57100</td>\n",
              "      <td>57400</td>\n",
              "      <td>51800</td>\n",
              "      <td>52500</td>\n",
              "      <td>1428801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-06</th>\n",
              "      <td>52000</td>\n",
              "      <td>53200</td>\n",
              "      <td>50200</td>\n",
              "      <td>51500</td>\n",
              "      <td>633818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-07</th>\n",
              "      <td>51600</td>\n",
              "      <td>52700</td>\n",
              "      <td>51200</td>\n",
              "      <td>51600</td>\n",
              "      <td>319603</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>248 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "와이지엔터테인먼트      시가     고가     저가     종가      거래량\n",
              "날짜                                             \n",
              "2019-10-08  23400  23900  22900  23800   148848\n",
              "2019-10-10  23550  23700  23000  23550   150525\n",
              "2019-10-11  23800  25200  23650  23950   474910\n",
              "2019-10-14  24350  25850  24150  25650   558471\n",
              "2019-10-15  25600  26700  25350  26000   425303\n",
              "...           ...    ...    ...    ...      ...\n",
              "2020-09-28  52300  57500  51800  57000  1067907\n",
              "2020-09-29  57700  58000  54900  58000   758762\n",
              "2020-10-05  57100  57400  51800  52500  1428801\n",
              "2020-10-06  52000  53200  50200  51500   633818\n",
              "2020-10-07  51600  52700  51200  51600   319603\n",
              "\n",
              "[248 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X8JE_P7RYbVb",
        "outputId": "61a70de3-2a0f-4760-8a6f-326caf671ad3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        }
      },
      "source": [
        "# 선형 모델 적용이 적합한지 확인해보기  (Scatter_plot의 모양이 선형일 수록, 선형모델적용이 타당한 것으로 해석 가능)\n",
        "\n",
        "from pandas.plotting import lag_plot\n",
        "\n",
        "fig, axes = plt.subplots(3, 2, figsize=(8, 12))\n",
        "plt.title('YG Autocorrelation plot')\n",
        "\n",
        "# The axis coordinates for the plots\n",
        "ax_idcs = [\n",
        "    (0, 0),\n",
        "    (0, 1),\n",
        "    (1, 0),\n",
        "    (1, 1),\n",
        "    (2, 0),\n",
        "    (2, 1)\n",
        "]\n",
        "\n",
        "for lag, ax_coords in enumerate(ax_idcs, 1):\n",
        "    ax_row, ax_col = ax_coords\n",
        "    axis = axes[ax_row][ax_col]\n",
        "    lag_plot(df['종가'], lag=lag, ax=axis)\n",
        "    axis.set_title(f\"Lag={lag}\")\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# 그래프의 해석 결과: \n",
        "# A linear shape to the plot suggests that an autoregressive model is probably a better choice.\n",
        "# An elliptical plot suggests that the data comes from a single-cycle sinusoidal model."
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x864 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlOSi35V683j",
        "outputId": "75222259-f65f-4288-8e99-135d3c0afe72",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.shape "
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(248, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RI71I9lyi0tv",
        "outputId": "36f6bdcf-b118-42a0-9498-55f6c7665726",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# train, validfate, testset으로 나누는데, 금융 시계열 데이터이므로, one-step forecast validation 필요 \n",
        "# https://neocarus.tistory.com/entry/%EB%AA%A8%ED%98%95%EC%9D%98-%EC%A0%81%ED%95%A9%EC%84%B1%EC%97%90-%EB%8C%80%ED%95%9C-%EA%B5%90%EC%B0%A8%EA%B2%80%EC%A6%9D%EC%9D%84-%EC%88%98%ED%96%89%ED%95%98%EB%8A%94-%EB%B0%A9%EB%B2%95 참고\n",
        "\n",
        "# train -> 선학습\n",
        "# validation_set의 one-step forecast validation 하기위한 과정\n",
        "# 이후, test개수만큼 predict 필요\n",
        "\n",
        "# 개수로 247개 -> train : 207개, validation : 30개, test : 10개로 개수로 데이터셋나눌수 있도록 \n",
        "\n",
        "print(df.shape)\n",
        "\n",
        "# 조정가능한 값 (validation_size , test_size)\n",
        "validation_size=40  # (2달)\n",
        "test_size=10        # (2주)\n",
        "\n",
        "train=df[:len(df)-validation_size]\n",
        "validation=df[len(df)-validation_size:len(df)-test_size]\n",
        "test=df[len(df)-test_size:]\n",
        "\n",
        "print(train.shape)\n",
        "print(validation.shape)\n",
        "print(test.shape)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(248, 5)\n",
            "(208, 5)\n",
            "(30, 5)\n",
            "(10, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RX96pJ-VkIbs"
      },
      "source": [
        "# column들 중, 종가만 고려한다\n",
        "train.drop(['시가', '고가','저가','거래량'], axis='columns', inplace=True)\n",
        "validation.drop(['시가', '고가','저가','거래량'], axis='columns', inplace=True)\n",
        "test.drop(['시가', '고가','저가','거래량'], axis='columns', inplace=True)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZrHy4zqjyL8",
        "outputId": "3f2fdad0-e4bb-47e1-b698-19f9994f5c33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 705
        }
      },
      "source": [
        "pip install pmdarima"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pmdarima\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/be/62/725b3b6ae0e56c77534de5a8139322e7b863ca53fd5bd6bd3b7de87d0c20/pmdarima-1.7.1-cp36-cp36m-manylinux1_x86_64.whl (1.5MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5MB 2.7MB/s \n",
            "\u001b[?25hCollecting setuptools<50.0.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c3/a9/5dc32465951cf4812e9e93b4ad2d314893c2fa6d5f66ce5c057af6e76d85/setuptools-49.6.0-py3-none-any.whl (803kB)\n",
            "\u001b[K     |████████████████████████████████| 808kB 17.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: urllib3 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (1.24.3)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (0.22.2.post1)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (0.16.0)\n",
            "Collecting Cython<0.29.18,>=0.29\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e7/d7/510ddef0248f3e1e91f9cc7e31c0f35f8954d0af92c5c3fd4c853e859ebe/Cython-0.29.17-cp36-cp36m-manylinux1_x86_64.whl (2.1MB)\n",
            "\u001b[K     |████████████████████████████████| 2.1MB 11.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (1.1.2)\n",
            "Collecting statsmodels<0.12,>=0.11\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/cb/83/540fd83238a18abe6c2d280fa8e489ac5fcefa1f370f0ca1acd16ae1b860/statsmodels-0.11.1-cp36-cp36m-manylinux1_x86_64.whl (8.7MB)\n",
            "\u001b[K     |████████████████████████████████| 8.7MB 38.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.6/dist-packages (from pmdarima) (1.18.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->pmdarima) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.19->pmdarima) (2.8.1)\n",
            "Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.6/dist-packages (from statsmodels<0.12,>=0.11->pmdarima) (0.5.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19->pmdarima) (1.15.0)\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: setuptools, Cython, statsmodels, pmdarima\n",
            "  Found existing installation: setuptools 50.3.0\n",
            "    Uninstalling setuptools-50.3.0:\n",
            "      Successfully uninstalled setuptools-50.3.0\n",
            "  Found existing installation: Cython 0.29.21\n",
            "    Uninstalling Cython-0.29.21:\n",
            "      Successfully uninstalled Cython-0.29.21\n",
            "  Found existing installation: statsmodels 0.10.2\n",
            "    Uninstalling statsmodels-0.10.2:\n",
            "      Successfully uninstalled statsmodels-0.10.2\n",
            "Successfully installed Cython-0.29.17 pmdarima-1.7.1 setuptools-49.6.0 statsmodels-0.11.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pkg_resources",
                  "statsmodels"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2eExisajjFk",
        "outputId": "08d57463-3e4e-4ea5-cd9a-1b228b6cf13f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from pmdarima import auto_arima\n",
        "import pmdarima as pm\n",
        "auto = pm.auto_arima(y=train, d=1, start_p=1, start_q=1,\n",
        "                            max_p=5, max_q=5, m=60,\n",
        "                            seasonal=True,\n",
        "                            D=1, trace=True,\n",
        "                            error_action='ignore',  \n",
        "                            suppress_warnings=True,\n",
        "                            stepwise=True,\n",
        "                            max_order=None)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Performing stepwise search to minimize aic\n",
            " ARIMA(1,1,1)(1,1,1)[60]             : AIC=inf, Time=nan sec\n",
            " ARIMA(0,1,0)(0,1,0)[60]             : AIC=2560.972, Time=1.37 sec\n",
            " ARIMA(1,1,0)(1,1,0)[60]             : AIC=2558.243, Time=8.34 sec\n",
            " ARIMA(0,1,1)(0,1,1)[60]             : AIC=inf, Time=nan sec\n",
            " ARIMA(1,1,0)(0,1,0)[60]             : AIC=2562.708, Time=0.75 sec\n",
            " ARIMA(1,1,0)(2,1,0)[60]             : AIC=2550.918, Time=43.61 sec\n",
            " ARIMA(1,1,0)(2,1,1)[60]             : AIC=inf, Time=nan sec\n",
            " ARIMA(1,1,0)(1,1,1)[60]             : AIC=inf, Time=nan sec\n",
            " ARIMA(0,1,0)(2,1,0)[60]             : AIC=2547.356, Time=37.36 sec\n",
            " ARIMA(0,1,0)(1,1,0)[60]             : AIC=2557.436, Time=5.11 sec\n",
            " ARIMA(0,1,0)(2,1,1)[60]             : AIC=inf, Time=nan sec\n",
            " ARIMA(0,1,0)(1,1,1)[60]             : AIC=inf, Time=nan sec\n",
            " ARIMA(0,1,1)(2,1,0)[60]             : AIC=2551.342, Time=46.91 sec\n",
            " ARIMA(1,1,1)(2,1,0)[60]             : AIC=inf, Time=213.26 sec\n",
            " ARIMA(0,1,0)(2,1,0)[60] intercept   : AIC=2550.850, Time=40.34 sec\n",
            "\n",
            "Best model:  ARIMA(0,1,0)(2,1,0)[60]          \n",
            "Total fit time: 397.248 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpsrB8Gjjq8W",
        "outputId": "43b2e120-10aa-4c0d-d17e-7f267e6e252c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "result_seasonal=auto.fit(train)\n",
        "print(result_seasonal.summary())"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                                 Statespace Model Results                                 \n",
            "==========================================================================================\n",
            "Dep. Variable:                                  y   No. Observations:                  208\n",
            "Model:             SARIMAX(0, 1, 0)x(2, 1, 0, 60)   Log Likelihood               -1270.678\n",
            "Date:                            Wed, 07 Oct 2020   AIC                           2547.356\n",
            "Time:                                    11:27:52   BIC                           2556.327\n",
            "Sample:                                         0   HQIC                          2551.001\n",
            "                                            - 208                                         \n",
            "Covariance Type:                              opg                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "ar.S.L60      -0.3012      0.047     -6.464      0.000      -0.393      -0.210\n",
            "ar.S.L120     -0.2606      0.041     -6.404      0.000      -0.340      -0.181\n",
            "sigma2      1.939e+06   2.65e+05      7.311      0.000    1.42e+06    2.46e+06\n",
            "===================================================================================\n",
            "Ljung-Box (Q):                       34.19   Jarque-Bera (JB):                 0.17\n",
            "Prob(Q):                              0.73   Prob(JB):                         0.92\n",
            "Heteroskedasticity (H):               1.66   Skew:                             0.08\n",
            "Prob(H) (two-sided):                  0.08   Kurtosis:                         3.06\n",
            "===================================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y37set89juhg",
        "outputId": "5962473d-5657-4f34-d83d-85c715f14f28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        }
      },
      "source": [
        "result_seasonal.plot_diagnostics(figsize=(12,10))          \n",
        "plt.show()  \n",
        "\n",
        "# 잡음(불규칙요인)의 분포 살피기 (표준잔차, 잔차분포, Normal Q-Q (QQ-Plot), 상관성)\n",
        "\n",
        "# https://data.library.virginia.edu/diagnostic-plots/ 참고\n",
        "\n",
        "# 표준화 잔차 (Standardized residual) : 추정 회귀식으로부터 관측치가 얼마나 떨어져 있는지, 절댓값 3이상이면 이상값이 확실, 연구자에 따라 2~3사이의 값을 이상값일 가능성이 달라짐.\n",
        "# 잔차분포 (Histogram plus estimated density) :  KDE - 커널밀도추정 (정규분포를 스무딩한 것) ( https://darkpgmr.tistory.com/147 참고 ) - 잔차분포(N(0,1))가 KDE와 비슷하다 즉, 잔차가 정규분포 다시말해 회귀모델의 가정중 하나인 잔차가 독립이라는 것을 따른다는 이야기  \n",
        "# QQ-plot (Normal-QQ) : 수집 데이터를 표준정규분포의 분위수와 비교하여 그리는 그래프 -> 데이터의 정규성 가정에 대한 검토를 가능하게 해줌 -> 집단이 정규성을 따른다면, 점들이 직선의 형태로 그려짐.\n",
        "# 상관성 (Correlogram): ACF 그래프 "
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ykk4BO5elcK1",
        "outputId": "8fbd62b6-9d92-4d61-e4d1-9e1925c8c22e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model = auto  # seeded from the model we've already fit (trainset으로 이미 학습된 모델)\n",
        "\n",
        "def forecast_one_step():\n",
        "    fc, conf_int = model.predict(n_periods=1, return_conf_int=True)\n",
        "    #print('fc : ',fc)\n",
        "    #print('conf_int : ',conf_int)\n",
        "    return (\n",
        "        fc.tolist()[0],\n",
        "        np.asarray(conf_int).tolist()[0])\n",
        "\n",
        "forecasts = []\n",
        "confidence_intervals = []\n",
        "errors = [] \n",
        "\n",
        "\n",
        "df_validation=df[validation.index[0]:validation.index[-1]]\n",
        "\n",
        "for i in range(len(df_validation['종가'])):\n",
        "  fc, conf = forecast_one_step()\n",
        "  forecasts.append(fc)\n",
        "  confidence_intervals.append(conf)\n",
        "  errors.append(fc-df_validation['종가'][i])\n",
        "  print('Error : ',fc-df_validation['종가'][i],' / y_pred : ',fc,' / y(종가) : ',df_validation['종가'][i],' / 저가 : ',df_validation['저가'][i],' / 고가 : ',df_validation['고가'][i])\n",
        "  # Updates the existing model with a small number of MLE steps\n",
        "  model.update(df_validation['종가'][i])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Error :  493.29447246261407  / y_pred :  44443.294472462614  / y(종가) :  43950  / 저가 :  43750  / 고가 :  45350\n",
            "Error :  -1159.0317709247538  / y_pred :  44440.968229075246  / y(종가) :  45600  / 저가 :  43400  / 고가 :  46100\n",
            "Error :  1701.031494506533  / y_pred :  47401.03149450653  / y(종가) :  45700  / 저가 :  44850  / 고가 :  46400\n",
            "Error :  1483.268996964296  / y_pred :  45733.268996964296  / y(종가) :  44250  / 저가 :  43600  / 고가 :  45800\n",
            "Error :  -4431.951700238053  / y_pred :  43818.04829976195  / y(종가) :  48250  / 저가 :  45700  / 고가 :  50800\n",
            "Error :  -983.8776544030989  / y_pred :  47516.1223455969  / y(종가) :  48500  / 저가 :  47500  / 고가 :  49450\n",
            "Error :  1747.8083630998299  / y_pred :  48497.80836309983  / y(종가) :  46750  / 저가 :  46050  / 고가 :  49300\n",
            "Error :  -2107.5746950878674  / y_pred :  47192.42530491213  / y(종가) :  49300  / 저가 :  46200  / 고가 :  49900\n",
            "Error :  2739.562824449502  / y_pred :  49239.5628244495  / y(종가) :  46500  / 저가 :  46000  / 고가 :  48800\n",
            "Error :  -1439.7100868656926  / y_pred :  45260.28991313431  / y(종가) :  46700  / 저가 :  44700  / 고가 :  47500\n",
            "Error :  -592.7953351970646  / y_pred :  46207.204664802935  / y(종가) :  46800  / 저가 :  45100  / 고가 :  47150\n",
            "Error :  -1474.4148983385821  / y_pred :  47225.58510166142  / y(종가) :  48700  / 저가 :  47000  / 고가 :  49350\n",
            "Error :  -1811.5112762099598  / y_pred :  48588.48872379004  / y(종가) :  50400  / 저가 :  48100  / 고가 :  50800\n",
            "Error :  -363.4697958271572  / y_pred :  50536.53020417284  / y(종가) :  50900  / 저가 :  49450  / 고가 :  51400\n",
            "Error :  1937.9106400801684  / y_pred :  50937.91064008017  / y(종가) :  49000  / 저가 :  47600  / 고가 :  52500\n",
            "Error :  2098.9258480004573  / y_pred :  48748.92584800046  / y(종가) :  46650  / 저가 :  46000  / 고가 :  49000\n",
            "Error :  -330.23765920417645  / y_pred :  48119.76234079582  / y(종가) :  48450  / 저가 :  46800  / 고가 :  49400\n",
            "Error :  -1810.9499917544672  / y_pred :  48389.05000824553  / y(종가) :  50200  / 저가 :  48700  / 고가 :  50600\n",
            "Error :  -1963.9561566321572  / y_pred :  50236.04384336784  / y(종가) :  52200  / 저가 :  49750  / 고가 :  52300\n",
            "Error :  154.17304333920765  / y_pred :  52454.17304333921  / y(종가) :  52300  / 저가 :  49650  / 고가 :  53000\n",
            "Error :  -3622.920779939981  / y_pred :  51077.07922006002  / y(종가) :  54700  / 저가 :  51900  / 고가 :  54900\n",
            "Error :  -4072.2966292860365  / y_pred :  53527.70337071396  / y(종가) :  57600  / 저가 :  54200  / 고가 :  58500\n",
            "Error :  631.312805208574  / y_pred :  58331.312805208574  / y(종가) :  57700  / 저가 :  56200  / 고가 :  60500\n",
            "Error :  -115.20759321980586  / y_pred :  57584.792406780194  / y(종가) :  57700  / 저가 :  56700  / 고가 :  59000\n",
            "Error :  -997.5566625855863  / y_pred :  57702.443337414414  / y(종가) :  58700  / 저가 :  56900  / 고가 :  60500\n",
            "Error :  745.404585850898  / y_pred :  58745.4045858509  / y(종가) :  58000  / 저가 :  57500  / 고가 :  59300\n",
            "Error :  489.2031374021244  / y_pred :  58489.203137402124  / y(종가) :  58000  / 저가 :  57000  / 고가 :  58400\n",
            "Error :  513.0461461836967  / y_pred :  58813.0461461837  / y(종가) :  58300  / 저가 :  57300  / 고가 :  58800\n",
            "Error :  2269.9277175548486  / y_pred :  58569.92771755485  / y(종가) :  56300  / 저가 :  55800  / 고가 :  58300\n",
            "Error :  -884.7959233735382  / y_pred :  57115.20407662646  / y(종가) :  58000  / 저가 :  55600  / 고가 :  58000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sDrnmpg4hRi7",
        "outputId": "b2d5fe9a-10bc-4c29-e2ad-87adf446067d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "errors_abs=np.abs(errors)\n",
        "\n",
        "print('오차절대값의 최대 : ',np.max(errors_abs))\n",
        "print('오차절대값의 최소 : ',np.min(errors_abs))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "오차절대값의 최대 :  4431.951700238053\n",
            "오차절대값의 최소 :  115.20759321980586\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cjUzvY-uhd98",
        "outputId": "f31b843a-1a77-4bba-d069-f9bf4aef688d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 965
        }
      },
      "source": [
        "df_validation"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>와이지엔터테인먼트</th>\n",
              "      <th>시가</th>\n",
              "      <th>고가</th>\n",
              "      <th>저가</th>\n",
              "      <th>종가</th>\n",
              "      <th>거래량</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>날짜</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-08-07</th>\n",
              "      <td>44950</td>\n",
              "      <td>45350</td>\n",
              "      <td>43750</td>\n",
              "      <td>43950</td>\n",
              "      <td>426223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-10</th>\n",
              "      <td>44900</td>\n",
              "      <td>46100</td>\n",
              "      <td>43400</td>\n",
              "      <td>45600</td>\n",
              "      <td>863517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-11</th>\n",
              "      <td>46150</td>\n",
              "      <td>46400</td>\n",
              "      <td>44850</td>\n",
              "      <td>45700</td>\n",
              "      <td>421615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-12</th>\n",
              "      <td>45750</td>\n",
              "      <td>45800</td>\n",
              "      <td>43600</td>\n",
              "      <td>44250</td>\n",
              "      <td>455990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-13</th>\n",
              "      <td>46150</td>\n",
              "      <td>50800</td>\n",
              "      <td>45700</td>\n",
              "      <td>48250</td>\n",
              "      <td>2380126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-14</th>\n",
              "      <td>48450</td>\n",
              "      <td>49450</td>\n",
              "      <td>47500</td>\n",
              "      <td>48500</td>\n",
              "      <td>712309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-18</th>\n",
              "      <td>49000</td>\n",
              "      <td>49300</td>\n",
              "      <td>46050</td>\n",
              "      <td>46750</td>\n",
              "      <td>720514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-19</th>\n",
              "      <td>46750</td>\n",
              "      <td>49900</td>\n",
              "      <td>46200</td>\n",
              "      <td>49300</td>\n",
              "      <td>857261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-20</th>\n",
              "      <td>48800</td>\n",
              "      <td>48800</td>\n",
              "      <td>46000</td>\n",
              "      <td>46500</td>\n",
              "      <td>642998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-21</th>\n",
              "      <td>46700</td>\n",
              "      <td>47500</td>\n",
              "      <td>44700</td>\n",
              "      <td>46700</td>\n",
              "      <td>425496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-24</th>\n",
              "      <td>46100</td>\n",
              "      <td>47150</td>\n",
              "      <td>45100</td>\n",
              "      <td>46800</td>\n",
              "      <td>258061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-25</th>\n",
              "      <td>47200</td>\n",
              "      <td>49350</td>\n",
              "      <td>47000</td>\n",
              "      <td>48700</td>\n",
              "      <td>397671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-26</th>\n",
              "      <td>48750</td>\n",
              "      <td>50800</td>\n",
              "      <td>48100</td>\n",
              "      <td>50400</td>\n",
              "      <td>586564</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-27</th>\n",
              "      <td>49950</td>\n",
              "      <td>51400</td>\n",
              "      <td>49450</td>\n",
              "      <td>50900</td>\n",
              "      <td>528784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-28</th>\n",
              "      <td>50900</td>\n",
              "      <td>52500</td>\n",
              "      <td>47600</td>\n",
              "      <td>49000</td>\n",
              "      <td>1298643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-31</th>\n",
              "      <td>48950</td>\n",
              "      <td>49000</td>\n",
              "      <td>46000</td>\n",
              "      <td>46650</td>\n",
              "      <td>748647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-01</th>\n",
              "      <td>47550</td>\n",
              "      <td>49400</td>\n",
              "      <td>46800</td>\n",
              "      <td>48450</td>\n",
              "      <td>657023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-02</th>\n",
              "      <td>49000</td>\n",
              "      <td>50600</td>\n",
              "      <td>48700</td>\n",
              "      <td>50200</td>\n",
              "      <td>461506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-03</th>\n",
              "      <td>51400</td>\n",
              "      <td>52300</td>\n",
              "      <td>49750</td>\n",
              "      <td>52200</td>\n",
              "      <td>574772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-04</th>\n",
              "      <td>49650</td>\n",
              "      <td>53000</td>\n",
              "      <td>49650</td>\n",
              "      <td>52300</td>\n",
              "      <td>732555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-07</th>\n",
              "      <td>53000</td>\n",
              "      <td>54900</td>\n",
              "      <td>51900</td>\n",
              "      <td>54700</td>\n",
              "      <td>583146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-08</th>\n",
              "      <td>54700</td>\n",
              "      <td>58500</td>\n",
              "      <td>54200</td>\n",
              "      <td>57600</td>\n",
              "      <td>896783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-09</th>\n",
              "      <td>57700</td>\n",
              "      <td>60500</td>\n",
              "      <td>56200</td>\n",
              "      <td>57700</td>\n",
              "      <td>821117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-10</th>\n",
              "      <td>58700</td>\n",
              "      <td>59000</td>\n",
              "      <td>56700</td>\n",
              "      <td>57700</td>\n",
              "      <td>470213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-11</th>\n",
              "      <td>57400</td>\n",
              "      <td>60500</td>\n",
              "      <td>56900</td>\n",
              "      <td>58700</td>\n",
              "      <td>456164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-14</th>\n",
              "      <td>59300</td>\n",
              "      <td>59300</td>\n",
              "      <td>57500</td>\n",
              "      <td>58000</td>\n",
              "      <td>329573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-15</th>\n",
              "      <td>57800</td>\n",
              "      <td>58400</td>\n",
              "      <td>57000</td>\n",
              "      <td>58000</td>\n",
              "      <td>270909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-16</th>\n",
              "      <td>58000</td>\n",
              "      <td>58800</td>\n",
              "      <td>57300</td>\n",
              "      <td>58300</td>\n",
              "      <td>290431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-17</th>\n",
              "      <td>58300</td>\n",
              "      <td>58300</td>\n",
              "      <td>55800</td>\n",
              "      <td>56300</td>\n",
              "      <td>427639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-18</th>\n",
              "      <td>56900</td>\n",
              "      <td>58000</td>\n",
              "      <td>55600</td>\n",
              "      <td>58000</td>\n",
              "      <td>288517</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "와이지엔터테인먼트      시가     고가     저가     종가      거래량\n",
              "날짜                                             \n",
              "2020-08-07  44950  45350  43750  43950   426223\n",
              "2020-08-10  44900  46100  43400  45600   863517\n",
              "2020-08-11  46150  46400  44850  45700   421615\n",
              "2020-08-12  45750  45800  43600  44250   455990\n",
              "2020-08-13  46150  50800  45700  48250  2380126\n",
              "2020-08-14  48450  49450  47500  48500   712309\n",
              "2020-08-18  49000  49300  46050  46750   720514\n",
              "2020-08-19  46750  49900  46200  49300   857261\n",
              "2020-08-20  48800  48800  46000  46500   642998\n",
              "2020-08-21  46700  47500  44700  46700   425496\n",
              "2020-08-24  46100  47150  45100  46800   258061\n",
              "2020-08-25  47200  49350  47000  48700   397671\n",
              "2020-08-26  48750  50800  48100  50400   586564\n",
              "2020-08-27  49950  51400  49450  50900   528784\n",
              "2020-08-28  50900  52500  47600  49000  1298643\n",
              "2020-08-31  48950  49000  46000  46650   748647\n",
              "2020-09-01  47550  49400  46800  48450   657023\n",
              "2020-09-02  49000  50600  48700  50200   461506\n",
              "2020-09-03  51400  52300  49750  52200   574772\n",
              "2020-09-04  49650  53000  49650  52300   732555\n",
              "2020-09-07  53000  54900  51900  54700   583146\n",
              "2020-09-08  54700  58500  54200  57600   896783\n",
              "2020-09-09  57700  60500  56200  57700   821117\n",
              "2020-09-10  58700  59000  56700  57700   470213\n",
              "2020-09-11  57400  60500  56900  58700   456164\n",
              "2020-09-14  59300  59300  57500  58000   329573\n",
              "2020-09-15  57800  58400  57000  58000   270909\n",
              "2020-09-16  58000  58800  57300  58300   290431\n",
              "2020-09-17  58300  58300  55800  56300   427639\n",
              "2020-09-18  56900  58000  55600  58000   288517"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dI1VasDn3Tb6",
        "outputId": "85100d43-8c22-44f7-87a1-192958910f56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 970
        }
      },
      "source": [
        "errors=pd.DataFrame(errors,columns={'오차'})\n",
        "errors=round(errors)  # 주가는 소수단위포함하지 않으므로 소수점 반올림 처리\n",
        "print('Average Of Error : ',np.mean(errors))\n",
        "errors"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average Of Error :  오차   -371.966667\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>오차</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>493.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1159.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1701.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1483.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-4432.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-984.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1748.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-2108.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2740.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-1440.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>-593.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>-1474.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>-1812.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>-363.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1938.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2099.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>-330.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>-1811.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>-1964.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>154.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>-3623.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>-4072.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>631.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>-115.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>-998.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>745.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>489.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>513.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2270.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>-885.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        오차\n",
              "0    493.0\n",
              "1  -1159.0\n",
              "2   1701.0\n",
              "3   1483.0\n",
              "4  -4432.0\n",
              "5   -984.0\n",
              "6   1748.0\n",
              "7  -2108.0\n",
              "8   2740.0\n",
              "9  -1440.0\n",
              "10  -593.0\n",
              "11 -1474.0\n",
              "12 -1812.0\n",
              "13  -363.0\n",
              "14  1938.0\n",
              "15  2099.0\n",
              "16  -330.0\n",
              "17 -1811.0\n",
              "18 -1964.0\n",
              "19   154.0\n",
              "20 -3623.0\n",
              "21 -4072.0\n",
              "22   631.0\n",
              "23  -115.0\n",
              "24  -998.0\n",
              "25   745.0\n",
              "26   489.0\n",
              "27   513.0\n",
              "28  2270.0\n",
              "29  -885.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pEjLhHhUTZjf",
        "outputId": "70b129d6-dc86-42ea-b98a-f03fdff5b84c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        }
      },
      "source": [
        "# 오차의 정규성 분포 확인\n",
        "\n",
        "fig = plt.figure(figsize=(12,6))\n",
        "ax1 = fig.add_subplot(2, 1, 1)\n",
        "ax2 = fig.add_subplot(2, 1, 2)\n",
        "\n",
        "sns.distplot(errors['오차'],ax=ax1)\n",
        "stats.probplot(errors['오차'], plot=ax2)\n",
        "plt.show()\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6HkMCBi2_Jf",
        "outputId": "91729870-bd4b-413f-bce0-972e57925666",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        }
      },
      "source": [
        "# 예측값과 실제값 차이그래프 확인\n",
        "\n",
        "plt.figure(figsize=(12, 3))\n",
        "plt.rcParams['lines.linewidth'] = 1\n",
        "plt.grid(axis='y')\n",
        "plt.bar(errors.index, errors['오차'],color='red')\n",
        "plt.title('Validation_Forecast Errors', fontsize=20)\n",
        "plt.xlabel('Days', fontsize=10)\n",
        "plt.ylabel('Errors', fontsize=10)\n",
        "plt.xticks(errors.index, fontsize=10)\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCbbIO5gOia-",
        "outputId": "ca881670-78d4-4234-f9dc-05b3e5560b7a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from numpy import array, mean\n",
        "from scipy.stats import sem, t\n",
        "\n",
        "\n",
        "def mean_confidence_interval(data, confidence=.999): \n",
        "    a = 1.0 * array(data) \n",
        "    n = len(a) \n",
        "    m, se = mean(a), sem(a)                      # mean : 평균, sem : 평균의 표준오차 \n",
        "    h = se * t._ppf((1 + confidence) / 2., n - 1) \n",
        "    return m, m - h, m + h\n",
        "\n",
        "\n",
        "avg,floor,cap=mean_confidence_interval(errors)\n",
        "\n",
        "error_lst=[]\n",
        "for i in range(len(errors['오차'])):\n",
        "  if (errors['오차'][i] > cap) | (errors['오차'][i] < floor):\n",
        "    error_lst.append((i,errors['오차'][i]))\n",
        "\n",
        "error_lst  "
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(2, 1701.0),\n",
              " (3, 1483.0),\n",
              " (4, -4432.0),\n",
              " (6, 1748.0),\n",
              " (7, -2108.0),\n",
              " (8, 2740.0),\n",
              " (12, -1812.0),\n",
              " (14, 1938.0),\n",
              " (15, 2099.0),\n",
              " (17, -1811.0),\n",
              " (18, -1964.0),\n",
              " (20, -3623.0),\n",
              " (21, -4072.0),\n",
              " (28, 2270.0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GsBPdlqGS4dg"
      },
      "source": [
        "temp=[i[0] for i in error_lst]\n",
        "\n",
        "change_points=pd.DataFrame({'종가':validation['종가'],'날짜':validation.index,'인덱스':range(0,30)})\n",
        "\n",
        "cp=[]\n",
        "for i,v in enumerate(change_points.index):\n",
        "  if i in temp:\n",
        "    cp.append(v.strftime(\"%Y-%m-%d\"))"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gFxE5EzRwSTv",
        "outputId": "949e68bd-49fe-4290-f7c4-c737c424eaac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "cp  # prophet에 들어갈 changingpoint 리스트"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['2020-08-11',\n",
              " '2020-08-12',\n",
              " '2020-08-13',\n",
              " '2020-08-18',\n",
              " '2020-08-19',\n",
              " '2020-08-20',\n",
              " '2020-08-26',\n",
              " '2020-08-28',\n",
              " '2020-08-31',\n",
              " '2020-09-02',\n",
              " '2020-09-03',\n",
              " '2020-09-07',\n",
              " '2020-09-08',\n",
              " '2020-09-17']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exb0RMRbpvAR"
      },
      "source": [
        "forecasts=pd.DataFrame(forecasts,index=validation.index,columns={'종가'})\n",
        "forecasts=round(forecasts)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "89Xb9_5s5ux6",
        "outputId": "5feba397-468b-4e71-c1e3-dfb17cca5b34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 965
        }
      },
      "source": [
        "forecasts"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>종가</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>날짜</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-08-07</th>\n",
              "      <td>44443.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-10</th>\n",
              "      <td>44441.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-11</th>\n",
              "      <td>47401.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-12</th>\n",
              "      <td>45733.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-13</th>\n",
              "      <td>43818.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-14</th>\n",
              "      <td>47516.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-18</th>\n",
              "      <td>48498.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-19</th>\n",
              "      <td>47192.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-20</th>\n",
              "      <td>49240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-21</th>\n",
              "      <td>45260.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-24</th>\n",
              "      <td>46207.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-25</th>\n",
              "      <td>47226.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-26</th>\n",
              "      <td>48588.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-27</th>\n",
              "      <td>50537.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-28</th>\n",
              "      <td>50938.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-31</th>\n",
              "      <td>48749.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-01</th>\n",
              "      <td>48120.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-02</th>\n",
              "      <td>48389.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-03</th>\n",
              "      <td>50236.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-04</th>\n",
              "      <td>52454.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-07</th>\n",
              "      <td>51077.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-08</th>\n",
              "      <td>53528.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-09</th>\n",
              "      <td>58331.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-10</th>\n",
              "      <td>57585.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-11</th>\n",
              "      <td>57702.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-14</th>\n",
              "      <td>58745.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-15</th>\n",
              "      <td>58489.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-16</th>\n",
              "      <td>58813.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-17</th>\n",
              "      <td>58570.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-18</th>\n",
              "      <td>57115.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 종가\n",
              "날짜                 \n",
              "2020-08-07  44443.0\n",
              "2020-08-10  44441.0\n",
              "2020-08-11  47401.0\n",
              "2020-08-12  45733.0\n",
              "2020-08-13  43818.0\n",
              "2020-08-14  47516.0\n",
              "2020-08-18  48498.0\n",
              "2020-08-19  47192.0\n",
              "2020-08-20  49240.0\n",
              "2020-08-21  45260.0\n",
              "2020-08-24  46207.0\n",
              "2020-08-25  47226.0\n",
              "2020-08-26  48588.0\n",
              "2020-08-27  50537.0\n",
              "2020-08-28  50938.0\n",
              "2020-08-31  48749.0\n",
              "2020-09-01  48120.0\n",
              "2020-09-02  48389.0\n",
              "2020-09-03  50236.0\n",
              "2020-09-04  52454.0\n",
              "2020-09-07  51077.0\n",
              "2020-09-08  53528.0\n",
              "2020-09-09  58331.0\n",
              "2020-09-10  57585.0\n",
              "2020-09-11  57702.0\n",
              "2020-09-14  58745.0\n",
              "2020-09-15  58489.0\n",
              "2020-09-16  58813.0\n",
              "2020-09-17  58570.0\n",
              "2020-09-18  57115.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNqq9CbK5ysP",
        "outputId": "fe24cbe1-8f90-4ddd-9bcc-ad48bd10348f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "test"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>와이지엔터테인먼트</th>\n",
              "      <th>종가</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>날짜</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-09-21</th>\n",
              "      <td>55600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-22</th>\n",
              "      <td>55200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-23</th>\n",
              "      <td>55900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-24</th>\n",
              "      <td>51900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-25</th>\n",
              "      <td>50900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-28</th>\n",
              "      <td>57000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-29</th>\n",
              "      <td>58000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-05</th>\n",
              "      <td>52500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-06</th>\n",
              "      <td>51500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-07</th>\n",
              "      <td>51600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "와이지엔터테인먼트      종가\n",
              "날짜               \n",
              "2020-09-21  55600\n",
              "2020-09-22  55200\n",
              "2020-09-23  55900\n",
              "2020-09-24  51900\n",
              "2020-09-25  50900\n",
              "2020-09-28  57000\n",
              "2020-09-29  58000\n",
              "2020-10-05  52500\n",
              "2020-10-06  51500\n",
              "2020-10-07  51600"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HeJHjXNGynVR"
      },
      "source": [
        "def MPE(y_true,y_pred):  # MPE지표는 따로 라이브러리를 못찾아서 함수를 만듦\n",
        "    return np.mean((y_true - y_pred) / y_true) * 100"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2K6v_ABEZNmt",
        "outputId": "ebe17dab-1b7d-46a7-ad65-2639391c2198",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        }
      },
      "source": [
        "# 현재 설정되어 있는 폰트 사이즈와 글꼴을 알아보자    ( https://colab.research.google.com/github/nicewook/datascience_exercise/blob/master/korean_font_on_matplotlib.ipynb#scrollTo=nzZ6wGntXBrP 참고)\n",
        "\n",
        "# Validation 그래프\n",
        "fig = plt.figure(figsize=(15,12))\n",
        "ax1 = fig.add_subplot(211)\n",
        "ax1.plot(validation['종가'],label='실제값',marker='o')\n",
        "ax1.plot(forecasts['종가'],color='red',label='예측값',marker='o')\n",
        "ax1.set_xlabel('Date')\n",
        "ax1.set_ylabel('Y')\n",
        "ax1.legend(loc=2)\n",
        "plt.show\n",
        "\n",
        "\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from pmdarima.metrics import smape\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "# https://partrita.github.io/posts/regression-error/ 참고\n",
        "\n",
        "print('Validation Accuracy : \\n')\n",
        "print(f\"Mean squared error(MSE): {mean_squared_error(validation, forecasts)}\")\n",
        "print(f\"Mean absolute error(MAE): {mean_absolute_error(validation, forecasts)}\")\n",
        "print(f\"Mean absolute percentage error(MAPE): {(mean_absolute_error(validation, forecasts))/validation.shape[0]*100}\")\n",
        "print(f\"MPE: {MPE(validation['종가'], forecasts['종가'])}\")\n",
        "print(f\"RMSE: {mean_squared_error(validation, forecasts)**0.5}\")\n",
        "print(f\"SMAPE: {smape(validation, forecasts)}\")"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Validation Accuracy : \n",
            "\n",
            "Mean squared error(MSE): 3461637.566666667\n",
            "Mean absolute error(MAE): 1505.5666666666666\n",
            "Mean absolute percentage error(MAPE): 5018.555555555556\n",
            "MPE: 0.6726019180721824\n",
            "RMSE: 1860.5476523504221\n",
            "SMAPE: 3.0121711412017627\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3itV9gCxuSK1"
      },
      "source": [
        "fc, conf_int = model.predict(n_periods=test.shape[0], return_conf_int=True)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gm2sWDWCvvUa"
      },
      "source": [
        "# 상한, 하한 담기\n",
        "cap=[]\n",
        "floor=[]\n",
        "for i in conf_int:\n",
        "  cap.append(i[1])\n",
        "  floor.append(i[0])"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KjAvpYn-vDcQ",
        "outputId": "1140d63c-6da9-455f-f2d1-5688e560c3ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "pred=pd.DataFrame({'yhat':fc,'upper':cap,'lower':floor},index=test.index)\n",
        "pred"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>yhat</th>\n",
              "      <th>upper</th>\n",
              "      <th>lower</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>날짜</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-09-21</th>\n",
              "      <td>58895.404011</td>\n",
              "      <td>61624.371382</td>\n",
              "      <td>56166.436641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-22</th>\n",
              "      <td>61230.447916</td>\n",
              "      <td>65089.790582</td>\n",
              "      <td>57371.105250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-23</th>\n",
              "      <td>61823.980954</td>\n",
              "      <td>66550.691091</td>\n",
              "      <td>57097.270816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-24</th>\n",
              "      <td>61021.335933</td>\n",
              "      <td>66479.270674</td>\n",
              "      <td>55563.401192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-25</th>\n",
              "      <td>63449.926993</td>\n",
              "      <td>69552.083541</td>\n",
              "      <td>57347.770444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-28</th>\n",
              "      <td>63507.911834</td>\n",
              "      <td>70192.489416</td>\n",
              "      <td>56823.334252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-29</th>\n",
              "      <td>63726.659975</td>\n",
              "      <td>70946.828972</td>\n",
              "      <td>56506.490977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-05</th>\n",
              "      <td>64262.806573</td>\n",
              "      <td>71981.491905</td>\n",
              "      <td>56544.121240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-06</th>\n",
              "      <td>65540.939335</td>\n",
              "      <td>73727.841446</td>\n",
              "      <td>57354.037225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-07</th>\n",
              "      <td>65374.275600</td>\n",
              "      <td>74004.028150</td>\n",
              "      <td>56744.523049</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    yhat         upper         lower\n",
              "날짜                                                  \n",
              "2020-09-21  58895.404011  61624.371382  56166.436641\n",
              "2020-09-22  61230.447916  65089.790582  57371.105250\n",
              "2020-09-23  61823.980954  66550.691091  57097.270816\n",
              "2020-09-24  61021.335933  66479.270674  55563.401192\n",
              "2020-09-25  63449.926993  69552.083541  57347.770444\n",
              "2020-09-28  63507.911834  70192.489416  56823.334252\n",
              "2020-09-29  63726.659975  70946.828972  56506.490977\n",
              "2020-10-05  64262.806573  71981.491905  56544.121240\n",
              "2020-10-06  65540.939335  73727.841446  57354.037225\n",
              "2020-10-07  65374.275600  74004.028150  56744.523049"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svs2bsIU3nf_",
        "outputId": "86131be5-2125-4960-cd53-62e5f7c24083",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "plt.figure(figsize=(12,7))\n",
        "plt.plot(df['종가'], 'green', color='blue', label='Training Data')\n",
        "plt.plot(validation.index,validation['종가'],color='green', marker='o', linestyle='dashed', label='Forecasted Price')\n",
        "plt.plot(test.index, test['종가'], color='black', label='Actual Price')\n",
        "plt.plot(pred.index, pred['yhat'], color='red', label='Predicted Price')\n",
        "plt.title('YG Stock Prices Prediction')\n",
        "plt.xlabel('Dates')\n",
        "plt.ylabel('Prices')\n",
        "#plt.xticks(np.arange(0,7982, 1300), df['Date'][0:7982:1300])\n",
        "plt.legend()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbab240e710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PP0N3xlh4RC_",
        "outputId": "fcd91986-9cf8-4b02-868b-e829c6c7b04f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "source": [
        "plt.figure(figsize=(12,7))\n",
        "plt.plot(pred.index, pred['yhat'], color='red',label='Predicted Price',marker='o')\n",
        "plt.plot(test.index, test['종가'], color='blue', label='Actual Price',marker='o')\n",
        "plt.legend()\n",
        "plt.title('YG Stock Prices Prediction')\n",
        "plt.xlabel('Dates')\n",
        "plt.ylabel('Prices')\n",
        "#plt.xticks(np.arange(6386,7982, 300), df['Date'][6386:7982:300])\n",
        "plt.legend()\n",
        "\n",
        "print('Testset Accuracy : \\n')\n",
        "print(f\"Mean squared error: {mean_squared_error(validation, forecasts)}\")\n",
        "\n",
        "print(f\"RMSE: {mean_squared_error(validation, forecasts)**0.5}\")\n",
        "print(f\"MPE: {MPE(validation['종가'], forecasts['종가'])}\")\n",
        "print(f\"SMAPE: {smape(validation, forecasts)}\")"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testset Accuracy : \n",
            "\n",
            "Mean squared error: 3461637.566666667\n",
            "RMSE: 1860.5476523504221\n",
            "MPE: 0.6726019180721824\n",
            "SMAPE: 3.0121711412017627\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYnlh2Oar0p8"
      },
      "source": [
        "'''\n",
        "<오차지표>\n",
        "https://m.blog.naver.com/pmw9440/221813530210 참고\n",
        "\n",
        "MS: mean squared error\n",
        "RMSE: root mean squared error\n",
        "MAE: mean absolute error\n",
        "MAPE: mean absolute percent error\n",
        "MDAPE: median absolute percent error\n",
        "SMAPE: \n",
        "\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5U51a-LXblj"
      },
      "source": [
        "'''\n",
        "n_steps = 5\n",
        "training_period = 0 + n_steps\n",
        "end = len(df) - n_steps - training_period - 10\n",
        "validation_size = 20\n",
        "start = end - validation_size\n",
        "print('start : ',start,'/ end : ',end,'/ validation_size : ',validation_size,'/ n_step : ',n_steps,'/ training_period : ',training_period)\n",
        "\n",
        "for i in range(start,end):\n",
        "  data = df[i:i+training_period+n_steps]\n",
        "\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}