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List of awesome papers about time series, mainly including algorithms based on machine learning | 收录时间序列分析中各个研究领域的高水平文章，主要包含基于机器学习的算法
| Date | Stars |
|---|---|
| 2026-07-31 | 491 |
| 2026-08-04 | 491 |
| 2026-08-06 | 491 |
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# Awesome Time Series Papers
> **Notice:** Thanks for all your interest. However, unfortunately, since I'm no longer working on time series analysis, **I may not be able to continue adding content by myself**. If someone **pulls requests**, I will update it. Better yet, if you are willing to **fork this repository and keep updating**, please contact me and I will add your repository link here. Thanks.
- Introduction:
- List of awesome papers from various research fields in time series analysis, mainly including algorithms based on machine learning.
- `*` after the title of the paper indicates that the full paper has been carefully read by [me](https://github.com/bighuang624).
- A link of open source code is given if avaliable on [Papers With Code](https://paperswithcode.com/) or [Github](https://github.com/).
- Some papers will give links to reading notes.
- Any [contributions](https://github.com/bighuang624/Time-Series-Papers/blob/master/.github/contribution_template.md) including PR are welcomed.
- 介绍：
- 收录时间序列分析中各个研究领域的高水平文章，主要包含基于机器学习的算法。
- 论文标题后的`*`号表示[我](https://github.com/bighuang624)仔细地读过全文。
- 如果 [Papers With Code](https://paperswithcode.com/) 或 [Github](https://github.com/) 上存在该论文的开源代码，则给出其链接。
- 有些论文会给出解读的链接。
- 欢迎包括 PR 在内的一切[贡献](https://github.com/bighuang624/Time-Series-Papers/blob/master/.github/contribution_template.md)。
- - -
## Catelog (目录) <!-- omit in toc -->
- [Awesome Time Series Papers](#awesome-time-series-papers)
- [Review (综述)](#review-%e7%bb%bc%e8%bf%b0)
- [2014](#2014)
- [2012](#2012)
- [Time Series Forecasting (时间序列预测)](#time-series-forecasting-%e6%97%b6%e9%97%b4%e5%ba%8f%e5%88%97%e9%a2%84%e6%b5%8b)
- [Univariate (单变量)](#univariate-%e5%8d%95%e5%8f%98%e9%87%8f)
- [2018](#2018)
- [Multivariate to Univariate (多变量预测单变量)](#multivariate-to-univariate-%e5%a4%9a%e5%8f%98%e9%87%8f%e9%a2%84%e6%b5%8b%e5%8d%95%e5%8f%98%e9%87%8f)
- [2018](#2018-1)
- [2017](#2017)
- [Multivariate to Multivariate (多变量预测多变量)](#multivariate-to-multivariate-%e5%a4%9a%e5%8f%98%e9%87%8f%e9%a2%84%e6%b5%8b%e5%a4%9a%e5%8f%98%e9%87%8f)
- [2018](#2018-2)
- [Time Series Classification (时间序列分类)](#time-series-classification-%e6%97%b6%e9%97%b4%e5%ba%8f%e5%88%97%e5%88%86%e7%b1%bb)
- [2018](#2018-3)
- [2017](#2017-1)
- [Time Series Clustering (时间序列聚类)](#time-series-clustering-%e6%97%b6%e9%97%b4%e5%ba%8f%e5%88%97%e8%81%9a%e7%b1%bb)
- [2019](#2019)
- [2018](#2018-4)
- [2016](#2016)
- [2015](#2015)
- [Anomaly Detection (异常检测)](#anomaly-detection-%e5%bc%82%e5%b8%b8%e6%a3%80%e6%b5%8b)
- [2019](#2019-1)
- [2018](#2018-5)
- [2017](#2017-2)
- [Sequence Modeling (序列建模)](#sequence-modeling-%e5%ba%8f%e5%88%97%e5%bb%ba%e6%a8%a1)
- [Supervised (有监督)](#supervised-%e6%9c%89%e7%9b%91%e7%9d%a3)
- [2018](#2018-6)
- [Unsupervised (无监督)](#unsupervised-%e6%97%a0%e7%9b%91%e7%9d%a3)
- [2019](#2019-2)
- [2018](#2018-7)
- [Query by Content (按内容查询)](#query-by-content-%e6%8c%89%e5%86%85%e5%ae%b9%e6%9f%a5%e8%af%a2)
- [Time Series Segmentation (时间序列分割)](#time-series-segmentation-%e6%97%b6%e9%97%b4%e5%ba%8f%e5%88%97%e5%88%86%e5%89%b2)
- [Motif Discovery (重复模式发现)](#motif-discovery-%e9%87%8d%e5%a4%8d%e6%a8%a1%e5%bc%8f%e5%8f%91%e7%8e%b0)
- [Study of Stock Market (股票市场研究)](#study-of-stock-market-%e8%82%a1%e7%a5%a8%e5%b8%82%e5%9c%ba%e7%a0%94%e7%a9%b6)
- [2017](#2017-3)
- [Spatio-temporal Forecasting (时空预测)](#spatio-temporal-forecasting-%e6%97%b6%e7%a9%ba%e9%a2%84%e6%b5%8b)
- [Traffic Prediction (交通预测)](#traffic-prediction-%e4%ba%a4%e9%80%9a%e9%a2%84%e6%b5%8b)
- [2018](#2018-8)
- [Others (其他)](#others-%e5%85%b6%e4%bb%96)
- [2019](#2019-3)
- [2018](#2018-9)
- - -
## Review (综述)
#### 2014
- **A review of unsupervised feature learning and deep learning for time-series modeling** [[paper](http://www.diva-portal.org/smash/get/diva2:710518/FULLTEXT02)]
#### 20Excerpt of 21,271 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0125fd8f67e7c47d, topic:awesome