TimeSeries
Arima, Sarima, LSTM, Prophet, DeepAR, Kats, Granger-causality, Autots
Install / Use
/learn @gulabpatel/TimeSeriesREADME
Awesome Time Series Forecasting/Prediction Papers
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. This repository is still being continuously improved. 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.
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.
univariate time series forecasting:
, where L is the history length, H is the prediction horizon length.
multivariate time series forecasting:
, where C is the number of variables (channels).
spatio-temporal forecasting:
, where N is the spatial dimension (number of measurement points).
irregular time series: observation/sampling times are irregular.
Some Additional Information.
🚩 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: 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.
Survey.
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 19-06-20|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|Arxiv 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 23-06-16|SSL|Arxiv 2023|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|Arxiv 2023|A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection|PaperList 23-10-09|BasicTS|Arxiv 2023|Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis|Benchmark 23-10-11|ProbTS|Arxiv 2023|ProbTS: A Unified Toolkit to Probe Deep Time-series Forecasting|Toolkit 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
Transformer.
Date|Method|Type|Conference|Paper Title and Paper Interpretation (In Chinese)|Code
-----|----|----|-----|-----|-----
19-06-29|LogTrans|
