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This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model.
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This repository contains a reading list of papers (400+ papers !!!) on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model. This repository is still being continuously improved. In addition to papers that have been accepted by top conferences or journals, the repository also includes the latest papers from arXiv. If you have found any relevant papers that need to be included in this repository, please feel free to submit a pull request (PR) or open an issue. If you find this repository useful, please give it a 🌟.
Each paper may apply to one or several types of forecasting, including univariate time series forecasting, multivariate time series forecasting, and spatio-temporal forecasting, which are also marked in the Type column. If covariates and exogenous variables are not considered, univariate time series forecasting involves predicting the future of one variable with the history of this variable, while multivariate time series forecasting involves predicting the future of C variables with the history of C variables. Note that repeating univariate forecasting multiple times can also achieve the goal of multivariate forecasting, which is called channel-independent. However, univariate forecasting methods cannot extract relationships between variables, so the basis for distinguishing between univariate and multivariate forecasting methods is whether the method involves interaction between variables. Besides, in the era of deep learning, many univariate models can be easily modified to directly process multiple variables for multivariate forecasting. And multivariate models generally can be directly used for univariate forecasting. Here we classify solely based on the model's description in the original paper. Spatio-temporal forecasting is often used in traffic and weather forecasting, and it adds a spatial dimension compared to univariate and multivariate forecasting. In spatio-temporal forecasting, if each measurement point has only one variable, it is equivalent to multivariate forecasting. Therefore, the distinction between spatio-temporal forecasting and multivariate forecasting is not clear. Spatio-temporal models can usually be directly applied to multivariate forecasting, and multivariate models can also be used for spatio-temporal forecasting with minor modifications. Here we also classify solely based on the model's description in the original paper.
Selected from shared topics, language and repository description—not editorial ratings.
ENSTA-U2IS-AI /
This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.
86/100 healthAnshMittal1811 /
This repository contains all the work that I regularly did and studied from Medium blogs, several research papers, and other Repos (related/unrelated to the research papers).
71/100 health
univariate time series forecasting: 
multivariate time series forecasting: 
spatio-temporal forecasting: 
irregular time series: observation/sampling times are irregular.🚩 2023/11/1: I have marked some recommended papers with 🌟 (Just my personal preference 😉).
🚩 2023/11/1: I have added a new category
🚩 2023/11/1: I also recommend you to check out some other GitHub repositories about awesome time series papers: time-series-transformers-review, awesome-AI-for-time-series-papers, time-series-papers, deep-learning-time-series.
🚩 2023/11/3: There are some popular toolkits or code libraries that integrate many time series models: PyPOTS, Time-Series-Library, Prophet, Darts, Kats, tsai, GluonTS, PyTorchForecasting, tslearn, AutoGluon, flow-forecast, PyFlux.
🚩 2023/12/28: Since the topic of LLM(Large Language Model)+TS(Time Series) has been popular recently, I have introduced a category (LLM) to include related papers. This is distinguished from the Pretrain category. Pretrain mainly contains papers which design agent tasks (contrastive or generative) suitable for time series, and only use large-scale time series data for pre-training.
🚩 2024/4/1: Some researchers have introduced the recently popular Mamba model into the field of time series forecasting, which can be found in the SSM (State Space Model) table.
🚩 2024/6/7: I will mark some hot papers with 🔥 (Papers with over 100 citations).
🚩 2024/9/10: I am preparing to open a new GitHub repository to collect papers related to Video Spatio-Temporal Forecasting (VSTF). The mapping function for VSTF is , where
and
are the height and width of each frame. Compared to spatio-temporal forecasting mentioned before, it replaces
with
. This setup is commonly used in video prediction and weather forecasting. Stay tuned!
🚩 2024/10/23: I have introduced a new table (Multimodal) to include papers that utilize multimodal data (such as relevant text) to assist in forecasting and a new table (KAN) to include papers that utilize Kolmogorov–Arnold Networks.
🚩 2024/12/30: Christoph Bergmeir raised insightful questions about the benchmarks in the field of time series forecasting during his talk at NIPS 2024. This critique is highly valuable and well worth watching. I strongly recommend watching this talk before embarking on time series research.
🚩 2025/06/02: I have divided the papers in the Pretrain & Representation Table into two groups: Representation Learning and Foundation Models. The former focuses on designing pretrain tasks (such as contrastive learning and masked modeling), while the latter typically provides time series foundation models pre-trained on large-scale time series datasets.
| Date | Method | Conference | Paper Title and Paper Interpretation (In Chinese) | Code |
|---|---|---|---|---|
| 15-11-23 | Multi-step | ACOMP 2015 | Comparison of Strategies for Multi-step-Ahead Prediction of Time Series Using Neural Network | None |
| 19-06-11 | STData🔥 | TKDE 2020 | Deep learning for spatio-temporal data mining: A survey | None |
| 19-06-20 | DL🔥 | SENSJ 2019 | A Review of Deep Learning Models for Time Series Prediction | None |
| 20-09-27 | DL🔥 | Arxiv 2020 | Time Series Forecasting With Deep Learning: A Survey | None |
| 22-02-15 | Transformer🔥 | IJCAI 2023 | Transformers in Time Series: A Survey | PaperList |
| 23-03-25 | STGNN🔥 | TKDE 2023 | Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey | None |
| 23-05-01 | Diffusion | Arxiv 2023 | Diffusion Models for Time Series Applications: A Survey | None |
| 23-06-14 | LargeST | NIPS 2023 | LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting | largest |
| 23-06-16 | SSL🔥 | TPAMI 2024 | Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects | None |
| 23-06-20 | OpenSTL | NIPS 2023 | OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning | Benchmark |
| 23-07-07 | GNN🔥 | TPAMI 2024 | A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection | PaperList |
| 23-10-09 | BasicTS | TKDE 2024 | Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis | BasicTS |
| 23-10-11 | ProbTS | Arxiv 2023 | ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons | ProbTS |
| 23-10-16 | LargeModel | Arxiv 2023 | Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook | PaperList |
| 23-12-28 | TSPP | Arxiv 2023 | TSPP: A Unified Benchmarking Tool for Time-series Forecasting | TSPP |
| 24-01-05 | Diffusion | Arxiv 2024 | The Rise of Diffusion Models in Time-Series Forecasting | None |
| 24-02-15 | LLM🔥 | Arxiv 2024 | Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review | None |
| 24-03-21 | FM | KDD 2024 | Foundation Models for Time Series Analysis: A Tutorial and Survey | None |
| 24-03-29 | TFB🌟🔥 | VLDB 2024 | TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods | TFB |
| 24-04-24 | Mamba-360 | Arxiv 2024 | Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and Challenges | Mamba-360 |
| 24-04-29 | [Diffusion](https:// |
SirRob1997 /
This repository contains the results for the paper: "Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers"
73/100 healthamanchadha /
This repository contains my solutions to the assignments for Stanford's CS231n "Convolutional Neural Networks for Visual Recognition" (Spring 2020).
64/100 healthjosedolz /
This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"
71/100 healthannamalai-nr /
This repository contains the "tensorflow" implementation of our paper "graph2vec: Learning distributed representations of graphs".
39/100 health