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📖 A curated list of awesome time-series papers, benchmarks, datasets, tutorials.
If you find any missed resources or errors, please feel free to open an issue or make a PR.
- [Sep. 22, 2024] Create the repo!
- 📚 Introduction and Tutorials
- 📝 Time-series Analysis Tasks and Related Papers
- 📦 Awesome Time-series Analysis Toolkits
- 🕶️ More Awesomeness
- Fast, Scalable, and Accurate Algorithms for Time-Series Analysis, Phd Thesis 2018. [Link]
- Fast Algorithms for Mining Co-evolving Time Series, Phd Thesis 2010. [Link]
- Foundation models for time series analysis: A tutorial and survey, in KDD 2024. [Paper]
- Position: What Can Large Language Models Tell Us about Time Series Analysis, in ICML 2024. [Paper]
- Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook, in arXiv 2023. [Paper] [Website]
- Transformers in Time Series: A Survey, in IJCAI 2023. [Paper] [GitHub Repo]
- Time series data augmentation for deep learning: a survey, in IJCAI 2021. [Paper]
- An Introduction to Machine Learning from Time Series, in ECML 2024. [Link]
- Time-Series Anomaly Detection: Overview and New Trends, in VLDB 2024. [Link] [Video]
- Out-of-Distribution Generalization in Time Series, in AAAI 2024. [Link] [Slides]
- Robust Time Series Analysis and Applications: An Interdisciplinary Approach, in ICDM 2023. [Link]
- TODO
- Agentic Retrieval-Augmented Generation for Time Series Analysis, in KDD 2024. [Paper]
- Moment: A family of open time-series foundation models, in ICML 2024. [Paper] [Code]
- A decoder-only foundation model for time-series forecasting, in ICML 2024. [Paper] [Code]
- TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting, in ICLR 2024. [Paper] [Code]
- Chronos: Learning the language of time series, in arXiv 2024. [Paper] [Code]
- Time-llm: Time series forecasting by reprogramming large language models, in ICLR 2024. [Paper] [Code]
- Lag-llama: Towards foundation models for time series forecasting, in NeurIPS Workshop 2023. [Paper] [Code]
- One fits all: Power general time series analysis by pretrained lm, in NeurIPS 2023. [Paper] [Code]
- Large Language Models Are Zero-Shot Time Series Forecasters, in NeurIPS 2023. [Paper] [Code]
- FITS: Modeling Time Series with 10k Parameters, in ICLR 2024. [Paper] [Code]
- Timesnet: Temporal 2d-variation modeling for general time series analysis, in ICLR 2023. [Paper] [Code]
- Ts2vec: Towards universal representation of time series, in AAAI 2022. [Paper] [Code]
- TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods, in VLDB 2024. [Paper]
- Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting, in ICLR 2023. [Paper] [Code]
- An Experimental Evaluation of Anomaly Detection in Time Series, in VLDB 2023. [Paper]
- Timesead: Benchmarking deep multivariate time-series anomaly detection, in TMLR 2023. [Paper]
- TSB-UAD: an end-to-end benchmark suite for univariate time-series anomaly detection, in VLDB 2022. [Paper] [Website]
- Anomaly detection in time series: a comprehensive evaluation, in VLDB 2022. [Paper] [Website]
- A review on outlier/anomaly detection in time series data, in CSUR 2021. [Paper]
- Anomaly detection for IoT time-series data: A survey, in IEEE Internet of Things Journal 2019. [Paper]
- TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data, in VLDB 2022. [Paper] [Code]
- Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy, in ICLR 2022. [Paper] [Code]
- Usad: Unsupervised anomaly detection on multivariate time series, in KDD 2020. [Paper] [Code]
- Robust anomaly detection for multivariate time series through stochastic recurrent neural network, in KDD 2019. [Paper] [Code]
- Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications, in WWW 2018. [Paper] [Code]
- Deep learning for time series classification: a review, in Data Mining and Knowledge Discovery 2019. [Paper]
- TODO
- End-to-end deep representation learning for time series clustering: a comparative study, in Data Mining and Knowledge Discovery 2023. [Paper]
- TODO
- Unsupervised Time Series Segmentation: A Survey on Recent Advances, in CMC 2024. [Paper]
- An Evaluation of Change Point Detection Algorithms, in arXiv 2022. [Paper] [Code]
- A survey of methods for time series change point detection, in Knowledge and information systems 2017. [Paper]
- ClaSP: parameter-free time series segmentation, in Data Mining and Knowledge Discovery 2023. [Paper] [Code]
- Matrix profile VIII: domain agnostic online semantic segmentation at superhuman performance levels, in ICDM 2017. [Paper] [Code]
- Espresso: Entropy and shape aware time-series segmentation for processing heterogeneous sensor data, in IMWUT 2020. [Paper] [Code]
- TSI-Bench: Benchmarking Time Series Imputation, in arXiv 2024. [Paper]
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Brits: Bidirectional recurrent imputation for time series, in NeurIPS 2018. [Paper] [Code]
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[Paper Title], in [J/C] [Year]. [Paper]
aeon: A toolkit for machine learning from time series. [Link]sktime: A unified framework for machine learning with time series. [Link]Kats: A toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis. [Link]tsai: State-of-the-art Deep Learning library for Time Series and Sequences. [Link]prophet: Tool for producing high quality forecasts for time series data. [Link]darts: A python library for user-friendly forecasting and anomaly detection on time series. [Link]gluonts: Probabilistic time series modeling in Python. [Link]pyts: A Python package for time series classification. [Link]
