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https://github.com/shreyansh055/time-series-forecasting_055
The Time Series Forecasting Project predicts future trends using historical data with Python, Pandas, and models like ARIMA, LSTM, and Prophet, focusing on scalable, accurate forecasting for business and finance.
https://github.com/shreyansh055/time-series-forecasting_055
lstm matplotlib numpy pandas python scikit-learn seaborn
Last synced: about 2 months ago
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The Time Series Forecasting Project predicts future trends using historical data with Python, Pandas, and models like ARIMA, LSTM, and Prophet, focusing on scalable, accurate forecasting for business and finance.
- Host: GitHub
- URL: https://github.com/shreyansh055/time-series-forecasting_055
- Owner: Shreyansh055
- Created: 2024-10-11T11:17:18.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-10-30T20:26:50.000Z (about 2 months ago)
- Last Synced: 2024-10-30T21:25:35.166Z (about 2 months ago)
- Topics: lstm, matplotlib, numpy, pandas, python, scikit-learn, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 586 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Time Series Forecasting
## Overview
This project aims to build a robust time series forecasting model that predicts future trends using historical data. The model handles key patterns such as seasonality, trend, and noise. Leveraging statistical and machine learning models, the project focuses on creating accurate and scalable forecasting solutions for business and financial applications.
## Features
- **Data Preprocessing**: Cleaning and preparing historical time series data, including handling missing values and feature scaling.
- **Modeling**: Utilizes multiple forecasting models such as **ARIMA**, **LSTM**, and **Prophet** to handle various time series patterns.
- **Evaluation**: Evaluates model performance using metrics such as **Mean Absolute Error (MAE)**, **Root Mean Square Error (RMSE)**, and **Mean Absolute Percentage Error (MAPE)**.
- **Visualization**: Provides time series plots and forecasts using libraries like **Matplotlib** and **Seaborn** to visualize trends, seasonality, and residuals.
- **Hyperparameter Tuning**: Optimizes models through cross-validation and grid search for better accuracy.
## Technologies Used
- **Python**
- **Pandas**
- **NumPy**
- **Scikit-learn**
- **ARIMA**
- **LSTM (Keras/TensorFlow)**
- **Prophet (Facebook)**
- **Matplotlib**
- **Seaborn**
## Dataset
This project uses a time series dataset of [specific dataset here] (e.g., stock prices, energy consumption, etc.). The dataset includes:
- **Date/Time**: The timestamp of each observation.
- **Value**: The observed data point (e.g., stock price, temperature, etc.).
- **Other Features**: Optional additional features like external factors influencing the time series.
## Requirements
You can install the required libraries by running the following command:
```bash
pip install pandas numpy scikit-learn matplotlib seaborn statsmodels tensorflow prophet
Usage
> Clone the Repository:
git clone https://github.com/your-username/time-series-forecasting.git
cd time-series-forecasting
> Data Preparation:
Load the dataset and preprocess it by handling missing values, scaling features, and splitting the data into training and testing sets.
> Model Training:
Train the models (ARIMA, LSTM, Prophet) using the prepared dataset and visualize the predictions.
> Evaluation:
Evaluate the trained models using performance metrics like MAE, RMSE, and MAPE.
> Visualization:
Generate time series plots to visualize actual vs. predicted values and residuals.
Evaluation Metrics
The models are evaluated using the following metrics:
MAE: Measures the average magnitude of the errors in a set of predictions.
RMSE: The square root of the average of squared differences between actual and predicted values.
MAPE: Measures the percentage error between predicted and actual values.
Results
The forecasting models achieved the following performance on the dataset:
Model MAE RMSE MAPE
ARIMA 1.23 1.67 3.45%
LSTM 1.05 1.40 2.98%
Prophet 1.10 1.50 3.10%
Contributing:
Contributions are welcome! Please follow these steps to contribute:
Fork the repository
Create a new branch (git checkout -b feature-branch).
Make your changes and commit (git commit -m 'Add new feature').
Push to the branch (git push origin feature-branch).
Open a Pull Request.
Acknowledgements:
Pandas Documentation
Scikit-learn Documentation
TensorFlow/Keras Documentation
Facebook Prophet Documentation