Time Series Library (TSLib)
TSLib is an open-source library for deep learning researchers, especially for deep time series analysis.
中文文档：README_zh.md
We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: long- and short-term forecasting, imputation, anomaly detection, and classification.
:triangular_flag_on_post:News (2026.04) Due to the limited bandwidth of the current maintainers, we will not be actively adding new features to this library. Since the library was originally released three years ago, many of its benchmarks may no longer be meaningful for evaluating the effectiveness or progress of current research. However, the baseline implementations remain correct. We therefore recommend seeking out newer benchmarks.
:triangular_flag_on_post:News (2025.12) Many thanks to the great work from ailuntz, which provides an updated requirements and docker deployment, as well as a well-organized document. This is quite meaningful to this project and beginners.
:triangular_flag_on_post:News (2025.11) Considering the rapid development of Large Time Series Models (LTSMs), we have newly added a [zero-shot forecasting] feature in TSLib. You can try this script to evaluate LTSMs.
:triangular_flag_on_post:News (2025.10) Given the recent confusion among researchers regarding minor improvements on standard benchmarks, we propose the [Accuracy Law] to characterize the objectives of deep time series forecasting tasks, which can be used to identify saturated datasets.
:triangular_flag_on_post:News (2024.10) We have included [TimeXer], which defined a practical forecasting paradigm: Forecasting with Exogenous Variables. Considering both practicability and computation efficiency, we believe the new forecasting paradigm defined in TimeXer can be the "right" task for future research.
:triangular_flag_on_post:News (2024.10) Our lab has open-sourced [OpenLTM], which provides a distinct pretrain-finetuning paradigm compared to TSLib. If you are interested in Large Time Series Models, you may find this repository helpful.
:triangular_flag_on_post:News (2024.07) We wrote a comprehensive survey of [Deep Time Series Models] with a rigorous benchmark based on TSLib. In this paper, we summarized the design principles of current time series models supported by insightful experiments, hoping to be helpful to future research.
:triangular_flag_on_post:News (2024.04) Many thanks for the great work from frecklebars. The famous sequential model Mamba has been included in our library. See this file, where you need to install mamba_ssm with pip at first.
:triangular_flag_on_post:News (2024.03) Given the inconsistent look-back length of various papers, we split the long-term forecasting in the leaderboard into two categories: Look-Back-96 and Look-Back-Searching. We recommend researchers read TimeMixer, which includes both look-back length settings in experiments for scientific rigor.
:triangular_flag_on_post:News (2023.10) We add an implementation to iTransformer, which is the state-of-the-art model for long-term forecasting. The official code and complete scripts of iTransformer can be found here.
:triangular_flag_on_post:News (2023.09) We added a detailed tutorial for TimesNet and this library, which is quite friendly to beginners of deep time series analysis.
:triangular_flag_on_post:News (2023.02) We release the TSlib as a comprehensive benchmark and code base for time series models, which is extended from our previous GitHub repository Autoformer.
Leaderboard for Time Series Analysis
Till March 2024, the top three models for five different tasks are:
Note: We will keep updating this leaderboard. If you have proposed advanced and awesome models, you can send us your paper/code link or raise a pull request. We will add them to this repo and update the leaderboard as soon as possible.
Compared models of this leaderboard. ☑ means that their codes have already been included in this repo.
- TimeXer - TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables [NeurIPS 2024] [Code]
- TimeMixer - TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting [ICLR 2024] [Code].
- TSMixer - TSMixer: An All-MLP Architecture for Time Series Forecasting [arXiv 2023] [Code]
- iTransformer - iTransformer: Inverted Transformers Are Effective for Time Series Forecasting [ICLR 2024] [Code].
- PatchTST - A Time Series is Worth 64 Words: Long-term Forecasting with Transformers [ICLR 2023] [Code].
- TimesNet - TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis [ICLR 2023] [Code].
- DLinear - Are Transformers Effective for Time Series Forecasting? [AAAI 2023] [Code].
- LightTS - Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures [arXiv 2022] [Code].
- ETSformer - ETSformer: Exponential Smoothing Transformers for Time-series Forecasting [arXiv 2022] [Code].
- Non-stationary Transformer - Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting [NeurIPS 2022] [Code].
- FEDformer - FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting [ICML 2022] [Code].
- Pyraformer - Pyraformer: Low-complexity Pyramidal Attention for Long-range Time Series Modeling and Forecasting [ICLR 2022] [Code].
- Autoformer - Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting [NeurIPS 2021] [Code].
- Informer - Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [AAAI 2021] [Code].
- Reformer - Reformer: The Efficient Transformer [ICLR 2020] [Code].
- Transformer - Attention is All You Need [NeurIPS 2017] [Code].
See our latest paper [TimesNet] for the comprehensive benchmark. We will release a real-time updated online version soon.
Newly added baselines. We will add them to the leaderboard after a comprehensive evaluation.
- MambaSL - MambaSL: Exploring Single-Layer Mamba for Time Series Classification [ICLR 2026] [Code]
- TimeFilter - TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting [ICML 2025] [Code]
- KAN-AD - KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks [ICML 2025] [Code]
- MultiPatchFormer - A multiscale model for multivariate time series forecasting [Scientific Reports 2025] [Code]
- WPMixer - WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecasting [AAAI 2025] [Code]
- MSGNet - MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting [AAAI 2024] [Code]
- PAttn - Are Language Models Actually Useful for Time Series Forecasting? [NeurIPS 2024] [Code]
- Mamba - Mamba: Linear-Time Sequence Modeling with Selective State Spaces [arXiv 2023] [Code]
- SegRNN - SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting [arXiv 2023] [Code].
- Koopa - Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors [NeurIPS 2023] [Code].
- FreTS - Frequency-domain MLPs are More Effective Learners in Time Series Forecasting [NeurIPS 2023] [Code].
- MICN - MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting [ICLR 2023][Code].
- Crossformer - Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting [ICLR 2023][Code].
- TiDE - Long-term Forecasting with TiDE: Time-series Dense Encoder [arXiv 2023] [Code].
- SCINet - SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction [NeurIPS 2022][Code].
- FiLM - FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting [NeurIPS 2022][Code].
- TFT - Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting [arXiv 2019][Code].
Newly added Large Time Series Models. This library also supports the zero-shot evaluation of the following LTSMs.
- Chronos2 - Chronos-2: From Univariate to Universal Forecasting [arXiv 2025] [Code]
- TiRex - TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning [NeurIPS 2025] [Code]
- Sundial - Sundial: A Family of Highly Capable Time Series Foundation Models [ICML 2025] [Code]
- Time-MoE - Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts [ICLR 2025] [Code]
- Toto - Toto: Time Series Optimized Transformer for Observability arXiv 2024
- Chronos - Chronos: Learning the Language of Time Series [TMLR 2024] [Code]
- Moirai - Unified Training of Universal Time Series Forecasting Transformers [ICML 2024]
- TimesFM - A decoder-only foundation model for time-series forecasting [ICML 2024] [Code]
Getting Started
Prepare Data
You can obtain the well-preprocessed datasets from [Google Drive], [Baidu Drive] or [Hugging Face]. Then place the downloaded data in the folder ./dataset.
Installation
Clone this repository.
git clone https://github.com/thuml/Time-Series-Library.git
cd Time-Series-Library
Create a new Conda environment.
conda create -n tslib python=3.11
conda activate tslib
Install Core Dependencies
⚠️ CUDA Compatibility Notice
The torch prebuilt package is CUDA-version specific. (See https://pytorch.org/get-started/previous-versions/)
Please make sure to install the package that matches your local CUDA version (e.g., cu118 or cu121).
Recommended: torch==2.5.1
pip install torch==2.5.1 --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt
Install Dependencies for Mamba Model (Required for Time-Series-Library/models/Mamba.py)
⚠️ Linux only
⚠️ CUDA Compatibility Notice
The prebuilt Mamba wheel is CUDA-version specific.
Please make sure to install the wheel that matches your local CUDA version
(e.g., cu11 or cu12). Installing a mismatched version may result in
runtime errors or import failures.
Example for CUDA 12:
pip install https://github.com/state-spaces/mamba/releases/download/v2.2.6.post3/mamba_ssm-2.2.6.post3+cu12torch2.5cxx11abiFALSE-cp311-cp311-linux_x86_64.whl
Install Dependencies for Moirai Model (Required for Time-Series-Library/models/Moirai.py)
pip install uni2ts --no-deps
Docker Deployment
# Build and start the Docker container in detached mode
docker compose -f 'Time-Series-Library/docker-compose.yml' up -d --build
# Download / place the dataset into a newly created folder ./dataset at the repository root
mkdir -p dataset # create the dataset directory
# Copy the local dataset into the container at /workspace/dataset
docker cp ./dataset tslib:/workspace/dataset
# Enter the running container to continue training / evaluation
docker exec -it tslib bash
# Switch to the workspace directory inside the container
cd /workspace
# Run zero-shot forecasting with the pre-trained Moirai model
python -u run.py \
--task_name zero_shot_forecast \ # task type: zero-shot forecasting
--is_training 0 \ # 0 = inference only (no training)
--root_path ./dataset/ETT-small/ \ # root directory of the dataset
--data_path ETTh1.csv \ # dataset file name
--model_id ETTh1_512_96 \ # experiment/model identifier
--model Moirai \ # model name (TimesFM / Moirai)
--data ETTh1 \ # dataset name
--features M \ # multivariate forecasting
--seq_len 512 \ # input sequence length
--pred_le
TSLib 是一个面向时间序列研究方向深度学习研究者的开源 Python 库，代码整洁，可用于评估先进的深度时间序列模型或开发自己的模型，在 GitHub 上已有约 13,000 颗星。