Introduction to NeuralForecast

The NeuralForecast library introduces a novel approach to time series forecasting through the implementation of Kolmogorov-Arnold Networks (KANs). This project benchmarks KANs against traditional models like multilayer perceptrons (MLPs) using extensive datasets, specifically the M3 and M4 datasets, which encompass over 102,000 unique time series across various frequencies.

Key Features of NeuralForecast

• Benchmarking: Comprehensive performance evaluation against MLPs and N-BEATS on M3 and M4 datasets.
• Efficiency: KANs reduce the number of parameters by 38% to 92% compared to MLPs.
• Reproducibility: Easy setup with conda environments and clear instructions for running experiments.
• Community Contributions: Open for contributions, with clear guidelines for reporting issues and submitting pull requests.

Technical Architecture and Implementation

The architecture of NeuralForecast is designed to facilitate efficient time series forecasting. KANs serve as a powerful alternative to MLPs, particularly in scenarios where parameter efficiency is crucial. The library is structured to support various forecasting tasks, leveraging the strengths of KANs while providing a robust framework for experimentation.

Installation Process

To get started with NeuralForecast, follow these steps:

1. Create a conda environment using the provided environment.yml file:

conda env create -f environment.yml

2. Activate the environment:

conda activate kan_benchmark

3. Run experiments with the desired dataset:

python run_experiment.py --dataset M3-yearly

Usage Examples and API Overview

NeuralForecast provides a straightforward API for users to implement time series forecasting. Here’s a quick example of how to run a forecasting experiment:

python run_experiment.py --dataset M4-monthly

This command will execute the forecasting process on the M4 monthly dataset, allowing users to evaluate the performance of KANs against other models.

Community and Contribution Aspects

NeuralForecast encourages community involvement. Users can report bugs, request features, or contribute to the codebase. The contribution guidelines are clearly outlined in the repository, making it easy for developers to get involved:

• Search for existing issues before reporting a bug.
• Submit feature requests with clear descriptions.
• Follow PR submission guidelines to ensure smooth integration of contributions.

License and Legal Considerations

NeuralForecast is licensed under the Apache License 2.0, allowing users to freely use, modify, and distribute the software while adhering to the terms outlined in the license. This ensures that the project remains open and accessible to all developers.

Conclusion

NeuralForecast represents a significant advancement in time series forecasting, leveraging the innovative KAN architecture. With its robust benchmarking against traditional models and a strong community focus, it stands as a valuable resource for developers and researchers alike.

For more information and to access the code, visit the NeuralForecast GitHub Repository.

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