Here’s a README file template for your time series forecasting project using Amazon SageMaker. This README provides an overview of the project, instructions for setup, and usage details.

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Time Series Forecasting with Amazon SageMaker

Project Overview

This project demonstrates how to build and deploy a time series forecasting model using Amazon SageMaker. The goal is to predict stock prices, sales, or demand using SageMaker's built-in DeepAR algorithm. The project includes data collection, model training, and deployment, as well as visualization of forecasted results.

Features

• Data Collection: Historical time series data is collected from Yahoo Finance using yfinance.
• Model Training: A time series forecasting model is trained using SageMaker's DeepAR algorithm.
• Deployment: The trained model is deployed to a SageMaker endpoint for making predictions.
• Visualization: Results are visualized using Plotly or Streamlit, comparing historical data with forecasted values.

Prerequisites

• AWS Account: An active AWS account with appropriate permissions.
• Python: Python 3.6 or later.
• AWS CLI: Configured with your AWS credentials.
• Boto3: AWS SDK for Python.
• SageMaker SDK: AWS SageMaker Python SDK.

Installation

1. Clone the Repository

   git clone https://github.com/yourusername/time-series-forecasting-sagemaker.git
   cd time-series-forecasting-sagemaker

2. Install Dependencies

   pip install -r requirements.txt

   requirements.txt:

   boto3
   sagemaker
   yfinance
   pandas
   matplotlib
   plotly
   

Contributing

Feel free to submit issues and pull requests. Contributions are welcome!

License

This project is licensed under the MIT License - see the LICENSE file for details.