This repository contains code and data for a time series forecasting project using Python. The project aims to predict future values of a time series dataset by training an XGBoost regressor.
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data: This folder contains the dataset used for time series forecasting. The main data file is "PJME_hourly.csv."
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timeseries_forecasting.ipynb: This notebook walks through the various steps of the time series forecasting project.
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Images: Output Image is stored in this folder.
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README.md: This file, which provides an overview of the project and its folder structure.
In this project, we perform the following tasks:
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Data loading and preprocessing: We load the time series data from the "PJME_hourly.csv" file, set the index to datetime, and create additional features for time series forecasting.
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Data visualization: We use Python libraries such as Matplotlib and Seaborn to visualize the time series data, the training and test splits, and various data patterns.
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Model training: We build an XGBoost regressor for time series forecasting, using features created in the preprocessing step.
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Feature importance: We analyze the feature importance of the XGBoost model to understand which features have the most impact on the predictions.
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Forecasting: We make predictions on the test set and visualize the predicted values alongside the true values.
You can explore the project by following the steps outlined in the "timeseries_forecasting.ipynb" notebook. This notebook provides a step-by-step guide to the entire time series forecasting process.
To run the code and notebooks in this project, you will need the following Python libraries and tools:
- pandas
- numpy
- matplotlib
- seaborn
- xgboost
- scikit-learn
You can install these libraries using pip or conda.
pip install pandas numpy matplotlib seaborn xgboost scikit-learn