Description
PatchTST is an official implementation of the "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers" paper. This AI model is designed for long-term time series forecasting, leveraging the power of Transformer architectures. The core innovation lies in its "patching" mechanism, which segments time series into subseries-level patches that are then treated as input tokens for the Transformer.
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Key design principles include channel-independence, where each channel represents a single univariate time series. These series share the same embedding and Transformer weights across all inputs, promoting efficient learning. The project is structured into two main folders: PatchTST_supervised and PatchTST_self_supervised, catering to different learning paradigms.
For supervised learning, users can install requirements, download datasets from sources like Autoformer, and initiate training using provided scripts. Hyperparameters such as patch length, look-back windows, and prediction lengths can be adjusted. The repository also includes codes for baseline models for comparative analysis.
In the self-supervised learning domain, PatchTST offers pre-training and fine-tuning capabilities. The pre-training script, `patchtst_pretrain.py`, trains the model using a specified mask ratio. The resulting pre-trained models are saved for downstream tasks. The `patchtst_finetune.py` script then facilitates either linear probing or full network fine-tuning on these pre-trained models.
The project acknowledges valuable contributions from several GitHub repositories, including LTSF-Linear, Informer2020, Autoformer, FEDformer, Pyraformer, RevIN, and tsai, highlighting its integration within the broader time series research community. This makes PatchTST a powerful tool for researchers and practitioners focused on accurate and scalable long-term time series forecasting.
- CategoryAI Models & LLMs
- Pricing ModelOpen source
- DeploymentCloud, Self-hosted
- PlatformsWeb
PatchTST Highlights
Transformer-based architecture for time series forecasting
Segmentation of time series into subseries-level patches
Channel-independence for efficient weight sharing
Official implementation of the PatchTST paper
Support for supervised learning
Support for self-supervised learning (pre-training and fine-tuning)
Adjustable hyperparameters for patch length, look-back, and prediction windows
Inclusion of baseline models for comparison
Codebase available on GitHub
Model weights saved for downstream tasks
Linear probing and full network fine-tuning options
Getting Started with PatchTST
Integrate: Access the GitHub repository for the PatchTST code.
Set up environment: Install necessary Python packages using `pip install -r requirements.txt`.
Download data: Obtain time series datasets and place them in a `./dataset` folder.
Supervised Training: Run training scripts from the `./scripts/PatchTST` directory, e.g., `sh ./scripts/PatchTST/weather.sh`.
Self-supervised Pre-training: Execute `python patchtst_pretrain.py` with dataset and mask ratio parameters.
Self-supervised Fine-tuning: Use `python patchtst_finetune.py` with the pre-trained model name and dataset.
PatchTST's Use Cases
- Long-term forecasting
- Time series analysis
- Forecasting with Transformers
- Self-supervised learning
- Supervised learning
- Multivariate forecasting
