DSIPTS Documentation¶
DSIPTS (Deep Learning for Time Series) is a Python library for time series forecasting that allows you to train state-of-the-art deep learning models on your time series data or on benchmark datasets from the literature.
Note
This project is under active development. Contributions and feedback are welcome!
Key Features¶
🚀 State-of-the-art Models: Includes Transformer-based models (Informer, Autoformer, PatchTST, iTransformer), convolutional models, RNNs, and more
📊 Flexible Data Handling: Support for multi-variate time series with D1/D2 layer architecture
⚡ PyTorch Lightning Integration: Built on PyTorch Lightning for scalable training
🔧 Easy Customization: Add your own architectures and compare against existing models
📈 Benchmark Datasets: Built-in support for popular time series benchmarks
Getting Started¶
User Guide¶
User Guide
Reference¶
API Reference¶
Examples¶
Examples & Tutorials
Development¶
Development