time_series/example_parametric_fit.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series - Parametric fit example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Links: [Covid-19 dataset (JohnsHopkinsUniversity)](https://github.com/CSSEGISandData/COVID-19)  |  [Article (AnalyticsVidhya)](https://medium.com/analytics-vidhya/how-to-predict-when-the-covid-19-pandemic-will-stop-in-your-country-with-python-d6fbb2425a9f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0 - Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Import pckgs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from ts_utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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",
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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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>3/23/20</th>\n",
       "      <th>3/24/20</th>\n",
       "      <th>3/25/20</th>\n",
       "      <th>3/26/20</th>\n",
       "      <th>3/27/20</th>\n",
       "      <th>3/28/20</th>\n",
       "      <th>3/29/20</th>\n",
       "      <th>3/30/20</th>\n",
       "      <th>3/31/20</th>\n",
       "      <th>4/1/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>74</td>\n",
       "      <td>84</td>\n",
       "      <td>94</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>120</td>\n",
       "      <td>170</td>\n",
       "      <td>174</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>104</td>\n",
       "      <td>123</td>\n",
       "      <td>146</td>\n",
       "      <td>174</td>\n",
       "      <td>186</td>\n",
       "      <td>197</td>\n",
       "      <td>212</td>\n",
       "      <td>223</td>\n",
       "      <td>243</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.0339</td>\n",
       "      <td>1.6596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>230</td>\n",
       "      <td>264</td>\n",
       "      <td>302</td>\n",
       "      <td>367</td>\n",
       "      <td>409</td>\n",
       "      <td>454</td>\n",
       "      <td>511</td>\n",
       "      <td>584</td>\n",
       "      <td>716</td>\n",
       "      <td>847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.5063</td>\n",
       "      <td>1.5218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>133</td>\n",
       "      <td>164</td>\n",
       "      <td>188</td>\n",
       "      <td>224</td>\n",
       "      <td>267</td>\n",
       "      <td>308</td>\n",
       "      <td>334</td>\n",
       "      <td>370</td>\n",
       "      <td>376</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.2027</td>\n",
       "      <td>17.8739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "0            NaN    Afghanistan  33.0000  65.0000        0        0        0   \n",
       "1            NaN        Albania  41.1533  20.1683        0        0        0   \n",
       "2            NaN        Algeria  28.0339   1.6596        0        0        0   \n",
       "3            NaN        Andorra  42.5063   1.5218        0        0        0   \n",
       "4            NaN         Angola -11.2027  17.8739        0        0        0   \n",
       "\n",
       "   1/25/20  1/26/20  1/27/20  ...  3/23/20  3/24/20  3/25/20  3/26/20  \\\n",
       "0        0        0        0  ...       40       74       84       94   \n",
       "1        0        0        0  ...      104      123      146      174   \n",
       "2        0        0        0  ...      230      264      302      367   \n",
       "3        0        0        0  ...      133      164      188      224   \n",
       "4        0        0        0  ...        3        3        3        4   \n",
       "\n",
       "   3/27/20  3/28/20  3/29/20  3/30/20  3/31/20  4/1/20  \n",
       "0      110      110      120      170      174     237  \n",
       "1      186      197      212      223      243     259  \n",
       "2      409      454      511      584      716     847  \n",
       "3      267      308      334      370      376     390  \n",
       "4        4        5        7        7        7       8  \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtf = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\", sep=\",\")\n",
    "dtf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### I'll select 1 country (ex. Italy) and look at 2 time series: \n",
    "- total cases per day\n",
    "- daily changes, i.e. new cases distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "## groupby country\n",
    "dtf = dtf.drop(['Province/State','Lat','Long'], axis=1).groupby(\"Country/Region\").sum().T\n",
    "\n",
    "## convert index to datetime\n",
    "dtf.index = pd.to_datetime(dtf.index, infer_datetime_format=True)\n",
    "\n",
    "## create total cases column\n",
    "dtf = pd.DataFrame(index=dtf.index, data=dtf[\"Italy\"].values, columns=[\"total\"])\n",
    "\n",
    "## create daily changes column\n",
    "dtf[\"new\"] = dtf[\"total\"] - dtf[\"total\"].shift(1)\n",
    "dtf[\"new\"] = dtf[\"new\"].fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total  new\n",
       "2020-01-22      0  0.0\n",
       "2020-01-23      0  0.0\n",
       "2020-01-24      0  0.0\n",
       "2020-01-25      0  0.0\n",
       "2020-01-26      0  0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>86498</td>\n",
       "      <td>5909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-28</td>\n",
       "      <td>92472</td>\n",
       "      <td>5974.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>97689</td>\n",
       "      <td>5217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-30</td>\n",
       "      <td>101739</td>\n",
       "      <td>4050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>105792</td>\n",
       "      <td>4053.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             total     new\n",
       "2020-03-27   86498  5909.0\n",
       "2020-03-28   92472  5974.0\n",
       "2020-03-29   97689  5217.0\n",
       "2020-03-30  101739  4050.0\n",
       "2020-03-31  105792  4053.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtf.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 - TimeSeries Analysis (cumulated cases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### I have to find the best curve to fit the data:\n",
    "- y = f(x)\n",
    "- x = t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, figsize=(13,7))\n",
    "ax[0].scatter(dtf.index, dtf[\"total\"].values, color=\"black\")\n",
    "ax[0].set(title=\"total cases\")\n",
    "ax[1].bar(dtf.index, dtf[\"new\"].values, color=\"black\")\n",
    "ax[1].set(title=\"new cases\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Total cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Linear function: f(x) = a + b*x\n",
    "'''\n",
    "def f(x):\n",
    "    return 10 + 1500*x\n",
    "\n",
    "y_linear = f(x=np.arange(len(dtf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Exponential function: f(x) = a + b^x\n",
    "'''\n",
    "def f(x):\n",
    "    return 10 + 1.18**x\n",
    "\n",
    "y_exponential = f(x=np.arange(len(dtf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Logistic function: f(x) = a / (1 + e^(-b*(x-c)))\n",
    "'''\n",
    "def f(x): \n",
    "    return 90000 / (1 + np.exp(-0.5*(x-20)))\n",
    "\n",
    "y_logistic = f(x=np.arange(len(dtf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13,5))\n",
    "ax.scatter(dtf[\"total\"].index, dtf[\"total\"].values, color=\"black\")\n",
    "ax.plot(dtf[\"total\"].index, y_linear, label=\"linear\", color=\"red\")\n",
    "ax.plot(dtf[\"total\"].index, y_exponential, label=\"exponential\", color=\"green\")\n",
    "ax.plot(dtf[\"total\"].index, y_logistic, label=\"logistic\", color=\"blue\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#-> Data starts low and grows exponentially ... looks similar to a exponential function. But I know for fact that\n",
    "#   this phenomenon has an upper limit, because the virus can't infect more than the total population of the country.\n",
    "#   Therefore the logistic function is more appropriate to model this data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### New cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Gaussian function: f(x) = a * e^(-0.5 * ((x-μ)/σ)**2)\n",
    "'''\n",
    "def f(x):\n",
    "    return 6000 * np.exp(-0.5 * ((x-60)/6)**2)\n",
    "\n",
    "y_gaussian = f(x=np.arange(len(dtf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(13,5))\n",
    "ax.bar(dtf[\"new\"].index, dtf[\"new\"].values, color=\"black\")\n",
    "ax.plot(dtf[\"new\"].index, y_gaussian, color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 - Model Desing & Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Total cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Function to fit. In this case logistic function:\n",
    "    f(x) = capacity / (1 + e^-k*(x - midpoint) )\n",
    "'''\n",
    "def f(X, c, k, m):\n",
    "    y = c / (1 + np.exp(-k*(X-m)))\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.29749197e+05, 1.85361042e-01, 6.11355473e+01])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Fit\n",
    "model = fit_curve(X=np.arange(len(dtf[\"total\"])), y=dtf[\"total\"].values, f=f, p0=[np.max(dtf[\"total\"]), 1, 1])\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- generating index date --> start: 2020-04-01 00:00:00 | end: 2020-04-29 00:00:00 | len: 29 ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Forecast\n",
    "preds = forecast_curve(dtf[\"total\"], f, model, pred_ahead=30, end=None, freq=\"D\", zoom=7, figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### New cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Function to fit. In this case gaussian function:\n",
    "    f(x) = a * e^(-0.5 * ((x-μ)/σ)**2)\n",
    "'''\n",
    "def f(X, a, b, c):\n",
    "    y = a * np.exp(-0.5 * ((X-b)/c)**2)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5790.32039854,   62.04015733,    9.09836419])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = fit_curve(X=np.arange(len(dtf[\"new\"])), y=dtf[\"new\"].values, f=f, p0=[1, np.mean(dtf[\"new\"]), np.std(dtf[\"new\"])])\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- generating index date --> start: 2020-04-01 00:00:00 | end: 2020-04-29 00:00:00 | len: 29 ---\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Forecast\n",
    "preds = forecast_curve(dtf[\"new\"], f, model, pred_ahead=30, end=None, freq=\"D\", zoom=7, figsize=(15,5))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}