# Time-Series-Library — Advanced Deep Learning Models for Time Series

> A comprehensive library of state-of-the-art deep learning models for time series analysis including forecasting, classification, imputation, and anomaly detection.

## Install

Save as a script file and run:

# Time-Series-Library — Advanced Deep Learning Models for Time Series

## Quick Use
```bash
pip install -r requirements.txt
python -u run.py --model TimesNet --data ETTh1 --pred_len 96
```

## Introduction
Time-Series-Library (TSLib) is an open-source Python library from Tsinghua University that provides a unified codebase for advanced deep learning models applied to time series tasks. It enables researchers and practitioners to benchmark and deploy models for forecasting, classification, imputation, and anomaly detection using a consistent interface.

## What Time-Series-Library Does
- Implements 15+ state-of-the-art deep time series models (TimesNet, PatchTST, iTransformer, DLinear, and more)
- Supports four core tasks: long-term forecasting, short-term forecasting, imputation, and anomaly detection
- Provides standardized experiment scripts for reproducible benchmarking
- Includes popular time series datasets with unified data loading pipelines
- Offers configurable training with GPU acceleration via PyTorch

## Architecture Overview
TSLib follows a modular design with separate layers for data loading, model definition, training, and evaluation. Each model implements a common interface, allowing users to switch between architectures by changing a single configuration parameter. The training loop handles multi-GPU setups and supports early stopping, learning rate scheduling, and checkpoint saving.

## Self-Hosting & Configuration
- Clone the repo and install dependencies via `pip install -r requirements.txt`
- Configure experiments through command-line arguments or shell scripts in the `scripts/` directory
- Set `--model` to choose from available architectures (TimesNet, PatchTST, DLinear, FEDformer, etc.)
- Adjust `--seq_len`, `--pred_len`, and `--label_len` to control input/output horizons
- Place custom datasets in the `dataset/` folder following the provided CSV format

## Key Features
- Unified codebase covering multiple time series tasks under one framework
- Reproducible baselines with pre-configured experiment scripts
- Supports Transformer-based, MLP-based, and CNN-based model families
- Active maintenance with new models added as they are published
- Clean PyTorch implementation suitable for extension and research

## Comparison with Similar Tools
- **Darts** — Higher-level API with statistical and ML models; TSLib focuses on deep learning research
- **sktime** — Scikit-learn-compatible framework for classical ML; TSLib targets neural architectures
- **GluonTS** — AWS probabilistic forecasting library; TSLib provides a broader model zoo
- **NeuralForecast** — Nixtla's neural forecasting; TSLib emphasizes multi-task support beyond forecasting

## FAQ
**Q: What Python and PyTorch versions are required?**
A: Python 3.8+ and PyTorch 1.8+ are recommended. Check requirements.txt for exact dependencies.

**Q: Can I add my own model to TSLib?**
A: Yes. Implement the `Model` class interface in the `models/` directory and register it in the experiment runner.

**Q: Does it support multivariate time series?**
A: Yes. Most models support both univariate and multivariate settings via the `--features` flag (S, M, or MS).

**Q: Is GPU training supported?**
A: Yes. Use `--use_gpu` and `--gpu` flags to select devices, with multi-GPU support available.

## Sources
- https://github.com/thuml/Time-Series-Library
- https://arxiv.org/abs/2306.06142

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