This project implements a time series forecasting model using Python to predict future values based on historical data. The model aims to provide insights into trends and patterns in the data for better decision-making.
Time series forecasting is crucial for various applications, including finance, sales forecasting, and resource management. This project utilizes statistical and machine learning techniques to analyze historical data and predict future outcomes.
-
Data Preparation:
- Load and preprocess the data.
- Handle missing values and outliers as necessary.
-
Model Selection:
- Choose appropriate forecasting models (e.g., ARIMA, Prophet, etc.).
- Train the model using historical data.
-
Evaluation:
- Evaluate the model's performance using metrics such as Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE).
- Perform cross-validation to ensure model robustness.
-
Visualization:
- Visualize actual vs. predicted values to assess the model's accuracy.
- Plot forecasted values along with confidence intervals.
- Pandas: For data manipulation and analysis.
- NumPy: For numerical operations.
- Statsmodels or Prophet: For time series forecasting.
- Matplotlib/Seaborn: For data visualization.
To run this project locally:
-
Clone this repository:
git clone https://github.com/ShivamVashisth28/Time-series-forecasting-model.git
-
Navigate to the project directory:
cd Time-Series-Forecasting-Model -
Install the required libraries:
pip install pandas numpy statsmodels matplotlib seaborn
-
Run the Python script:
python forecasting_model.py
The forecasting model predicts future values with a certain level of accuracy, demonstrating the underlying trends in the historical data.
The model performance is evaluated using:
- Mean Absolute Error (MAE)
- Root Mean Squared Error (RMSE)
Feel free to submit pull requests or open issues for any improvements or bug fixes.
This project is licensed under the MIT License.