List of research papers focus on time series forecasting and deep learning, as well as other resources like competitions, datasets, courses, blogs, code, etc.

• Papers
• Blogs
• Competitions
• Courses
• Libraries
• Datasets
• Books
• Repositories
• Tutorials

• SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling

  • 2 Feb 2023, Jiaxiang Dong, et al.

• PrimeNet : Pre-Training for Irregular Multivariate Time Series

  • AAAI 2023, Ranak Roy Chowdhury, et al.

  • [Official Code]

• Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement

  • 8 Jan 2023, Yan Li, et al.

  • [Official Code]

• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution

  • 5 Jan 2023, Yan Li, et al.

  • [Official Code]

• End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation

  • 28 Dec 2022, Shiyu Wang, et al.

  • [Official Code]

• Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting

  • 18 Dec 2022, Slawek Smyl, et al.

  • [Official Code]

• First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting

  • 15 Dec 2022, Xiyuan Zhang, et al.

  • [Code]

• Put Attention to Temporal Saliency Patterns of Multi-Horizon Time Series

  • 15 Dec 2022, Nghia Duong-Trung, et al.

  • [Official Code]

• Area2Area Forecasting: Looser Constraints, Better Predictions (Manuscript submitted to journal Information Sciences)

  • [Official Code]

• GBT: Two-stage Transformer Framework for Non-stationary Time Series Forecasting (Manuscript submitted to Information Sciences)

  • [Official Code]

• A K-variate Time Series Is Worth K Words: Evolution of the Vanilla Transformer Architecture for Long-term Multivariate Time Series Forecasting

  • 6 Dec 2022, Zanwei Zhou, et al.

  • [Official Code]

• DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting

  • 6 Dec 2022, Shiyong Lan, et al.

  • [Official Code]

• Learning of Cluster-based Feature Importance for Electronic Health Record Time-series

  • 06 Dec 2022, Henrique Aguiar, et al.

  • [Official Code]

• FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecasting

  • 2 Dec 2022, Maowei Jiang, et al.

  • [Official Code]

• MHCCL: Masked Hierarchical Cluster-wise Contrastive Learning for Multivariate Time Series

  • 2 Dec 2022, Qianwen Meng, et al.

  • [Official Code]

• AirFormer: Predicting Nationwide Air Quality in China with Transformers

  • 29 Nov 2022, Yuxuan Liang, et al.

  • [Official Code]

• Learning Latent Seasonal-Trend Representations for Time Series Forecasting

  • 29 Nov 2022, Zhiyuan Wang, et al.

  • [Official Code]

• A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

  • 27 Nov 2022, Yuqi Nie, et al.

  • [Official Code]

• Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks

  • 1 Nov 2022, Yijing Liu, et al.

• Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting

  • 1 Nov 2022, Yuzhou Chen, et al.

• TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting

  • 26 Oct 2022, Hyunwook Lee, et al.

• WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting

  • 25 Oct 2022, Youngin Cho, et al.

  • [Official Code]

• Retrieval Based Time Series Forecasting

  • 27 Sep 2022, Baoyu Jing, et al.

• FDNet: Focal Decomposed Network for Efficient, Robust and Practical Time Series Forecasting

  • 22 Sep 2022, Li Shen, et al.

  • [Official Code]

• Expressing Multivariate Time Series as Graphs with Time Series Attention Transformer

  • 19 Aug 2022, William T. Ng, et al.

  • [Official Code]

• Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting

  • 10 Aug 2022, Zezhi Shao, et al.

  • [Official Code]

• Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect

  • 22 Jul 2022, Li Shen, et al.

  • [Official Code]

• Formal Algorithms for Transformers

  • 19 Jul 2022, Mary Phuong, Marcus Hutter

• Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes

  • 13 Jul 2022, Gregory Benton, et al.

  • [Official Code]

• Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures

  • 4 Jul 2022, Tianping Zhang, et al.

• Utilizing Expert Features for Contrastive Learning of Time-Series Representations

  • 23 Jun 2022, Manuel Nonnenmacher, et al.

  • [Official Code]

• Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency

  • 17 Jun 2022, Xiang Zhang, et al.

  • [Official Code]

• Closed-Form Diffeomorphic Transformations for Time Series Alignment

  • 16 Jun 2022, Iñigo Martinez, et al.

  • [Official Code]

• Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting

  • 8 Jun 2022, Amin Shabani, et al.

  • [Official Code]

• Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting

  • 28 May 2022, Yong Liu, et al.
  • [Official Code]

• Are Transformers Effective for Time Series Forecasting?

  • 26 May 2022, Ailing Zeng, et al.

  • [Official Code]

• FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting

  • 18 May 2022, Tian Zhou, et al.

  • [Official Code]

• EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting

  • 25 April 2022, Sheo Yon Jhin, et al.

  • [Official Code]

• RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph

  • 25 April 2022, Ruijie Wang, et al.

  • [Official Code]

• DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting

  • 15 Mar 2022, Wei Fan, et al.

  • [Official Code]

• Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting

  • 23 Feb 2022, Dazhao Du, et al.

  • [Code]

• SAITS: Self-Attention-based Imputation for Time Series

  • 17 Feb 2022, Wenjie Du, et al.

  • [Official Code]

• Adaptive Conformal Predictions for Time Series

  • 15 Feb 2022, Margaux Zaffran, et al.

  • [Official Code]

• ST-GSP: Spatial-Temporal Global Semantic Representation Learning for Urban Flow Prediction

  • 15 Feb 2022, Liang Zhao, et al.

  • [Official Code]

• Transformers in Time Series: A Survey

  • 15 Feb 2022, Qingsong Wen, et al.

  • [Official Code]

• TACTiS: Transformer-Attentional Copulas for Time Series

  • 7 Feb 2022, Alexandre Drouin, et al.

  • [Official Code]

• CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

  • 3 Feb 2022, Gerald Woo, et al.

  • [Official Code]

• ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

  • 3 Feb 2022, Gerald Woo, et al.

  • [Official Code]

• FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

  • 30 Jan 2022, Tian Zhou, et al.

  • [Official Code]

• N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

  • 30 Jan 2022, Cristian Challu, et al.

  • [Official Code]

• TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs

  • 15 Dec 2021, Yushan Liu, et al.

  • [Official Code]

• Modeling Irregular Time Series with Continuous Recurrent Units

  • 22 Nov 2021, Mona Schirmer, et al.

  • [Official Code]

• Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series

  • 4 Nov 2021, Daniel Kramer, et al.

  • [Official Code]

• ClaSP - Time Series Segmentation

  • 30 Oct 2021, Patrick Schäfer, et al.

  • [Official Code]

• HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information

  • 26 Oct 2021, Wentao Xu, et al.

  • [Official Code]

• Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting

  • 13 Oct 2021, Kiran Madhusudhanan, et al.

  • [Official Code]

• CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning

  • 30 Sep 2021, Garrett Wilson, et al.

  • [Official Code]

• Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting

  • 29 Sep 2021, Shizhan Liu, et al.

  • [Code]

• Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift

  • 29 Sep 2021, Taesung Kim, et al.

  • [Official Code]

• DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications

  • 23 Sep 2021, Dongqi Han, et al.

  • [Official Code]

• CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting

  • 15 Sep 2021, Harshavardhan Kamarthi, et al.

  • [Official Code]

• Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation

  • 10 Sep 2021, Ziluo Ding, et al.

  • [Official Code]

• TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting

  • 29 Aug 2021, Li Shen, Yangzhu Wang

  • [Official Code]

• Machine learning in the Chinese stock market

  • 27 Aug 2021, Markus Leippold, et al.

• Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization

  • 14 Aug 2021, Ahmed Abdulaal, et al.

  • [Official Code]

• Time-Series Representation Learning via Temporal and Contextual Contrasting

  • 26 Jun 2021, Emadeldeen Eldele, et al.

  • [Official Code]

• Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

  • 24 Jun 2021, Haixu Wu, et al.

  • [Code]

• TS2Vec: Towards Universal Representation of Time Series

  • 19 Jun 2021, Zhihan Yue, et al.

  • [Code]

• Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction

  • 17 Jun 2021, Minhao Liu, et al.

  • [Code]

• Voice2Series: Reprogramming Acoustic Models for Time Series Classification

  • 17 Jun 2021, Chao-Han Huck Yang, et al.

  • [Official Code]

• Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding

  • 1 Jun 2021, Sana Tonekaboni, et al.

  • [Official Code]

• Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting

  • 10 May 2021, Yuzhou Chen, et al.

  • [Official Code]

• Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

  • 12 Apr 2021, Kin G. Olivares, et al.

  • [Code]

• FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection

  • 8 March 2021, Jia Li, et al.

  • [Official Code]

• Perceiver: General Perception with Iterative Attention

  • 4 Mar 2021, Andrew Jaegle, et al.

  • [Official Code] [Community Code]

• Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series

  • 3 Mar 2021, Yinjun Wu, et al.

  • [Official Code]

• Domain Adaptation for Time Series Forecasting via Attention Sharing

  • 13 Feb 2021, Xiaoyong Jin, et al.

  • [Official Code]

• Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting

  • 31 Jan 2021, Longyuan Li, et al.

  • [Official Code]

• Long Horizon Forecasting With Temporal Point Processes

  • 8 Jan 2021, Prathamesh Deshpande, et al.

  • [Official Code]

• Do We Really Need Deep Learning Models for Time Series Forecasting?

  • 6 Jan 2021, Shereen Elsayed, et al.

  • [Code]

• Conditional Local Convolution for Spatio-temporal Meteorological Forecasting

  • 4 Jan 2021, Haitao Lin, et al.

  • [Official Code]

• Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

  • 14 Dec 2020, Haoyi Zhou, et al.

  • [Code]

• A Transformer-based Framework for Multivariate Time Series Representation Learning

  • 6 Oct 2020, George Zerveas, et al.

  • [Code]

• Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting

  • 10 Sep 2020, Amirreza Farnoosh, et al.

  • [Official Code]

• Deep Learning for Anomaly Detection: A Review

  • 6 Jul 2020, Guansong Pang, et al.

• Time Series Data Augmentation for Deep Learning: A Survey

  • 27 Feb 2020, Qingsong Wen, et al.

• Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

  • 19 Dec 2019, Bryan Lim, et al.

  • [Code]

• Time-Series Aware Precision and Recall for Anomaly Detection: Considering Variety of Detection Result and Addressing Ambiguous Labeling

  • 3 Nov 2019, Won-Seok Hwang, et al.

  • [Official Code]

• High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes

  • 7 Oct 2019, David Salinas, et al.

  • [Code]

• Time2Vec: Learning a Vector Representation of Time

  • 11 Jul 2019, Seyed Mehran Kazemi, et al.

  • [Code]

• Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting

  • 29 Jun 2019, Shiyang Li, et al.

  • [Code] [Community Code]

• N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

  • 24 May 2019, Boris N. Oreshkin, et al.

  • [Code]

• Time-Series Event Prediction with Evolutionary State Graph

  • 10 May 2019, Wenjie Hu, et al.

  • [Official Code]

• Unsupervised Scalable Representation Learning for Multivariate Time Series

  • 30 Jan 2019, Jean-Yves Franceschi, et al.

  • [Official Code]

• Universal features of price formation in financial markets: perspectives from Deep Learning

  • 19 Mar 2018, Justin Sirignano, Rama Cont

• Graph Attention Networks

  • 30 Oct 2017, Petar Veličković, et al.

  • [Code]

• Attention Is All You Need

  • 12 Jun 2017, Ashish Vaswani, et al.

  • [Code]

• DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

  • 13 Apr 2017, David Salinas, et al.

  • [Code]

• CS25: Transformers United

• The Annotated Transformer
• The Illustrated Transformer

• GoDaddy - Microbusiness Density Forecasting | Kaggle

• JPX Tokyo Stock Exchange Prediction | Kaggle

• Ubiquant Market Prediction | Kaggle

• CS25: Transformers United

• arch

  • Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for financial econometrics, written in Python (with Cython and/or Numba used to improve performance)

• AutoTS

  • AutoTS is a time series package for Python designed for rapidly deploying high-accuracy forecasts at scale.

• BasicTS

  • BasicTS (Basic Time Series) is a PyTorch-based benchmark and toolbox for time series forecasting (TSF).

• Beibo

  • Beibo is a Python library that uses several AI prediction models to predict stocks returns over a defined period of time.

• Cesium

  • Cesium is an end-to-end machine learning platform for time-series, from calculation of features to model-building to predictions.

• Darts

  • Darts is a Python library for easy manipulation and forecasting of time series.

• DeepOD

  • DeepOD is an open-source python framework for deep learning-based anomaly detection on multivariate data.

• Flow Forecast

  • Flow Forecast is a deep learning PyTorch library for time series forecasting, classification, and anomaly detection.

• GluonTS

  • GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models.

• Greykite

  • The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.

• HyperTS

  • A Full-Pipeline Automated Time Series (AutoTS) Analysis Toolkit.

• Kats

  • Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis.

• Merlion

  • Merlion is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance.

• NeuralForecast

  • NeuralForecast is a Python library for time series forecasting with deep learning models.

• NeuralProphet

  • A Neural Network based Time-Series model, inspired by Facebook Prophet and AR-Net, built on PyTorch.

• PaddleTS

  • PaddlePaddle-based Time Series Modeling in Python.

• Prophet

  • Prophet is a forecasting procedure implemented in R and Python. It is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts.

• PyDaddy

  • A Python package to discover stochastic differential equations from time series data.

• PyDMD: Python Dynamic Mode Decomposition

  • PyDMD is a Python package that uses Dynamic Mode Decomposition for a data-driven model simplification based on spatiotemporal coherent structures.

• PyPOTS

  • A Python Toolbox for Data Mining on Partially-Observed Time Series.

• Python Outlier Detection (PyOD)

  • PyOD is a comprehensive and scalable Python library for outlier detection (anomaly detection)

• PyTorch Forecasting

  • PyTorch Forecasting is a PyTorch-based package for forecasting time series with state-of-the-art network architectures.

• PyTorchTS

  • PyTorchTS is a PyTorch Probabilistic Time Series forecasting framework which provides state of the art PyTorch time series models by utilizing GluonTS as its back-end API and for loading, transforming and back-testing time series data sets.

• pyts

  • pyts is a Python package dedicated to time series classification.

• Qlib

  • Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.

• RQAlpha

  • A extendable, replaceable Python algorithmic backtest & trading framework supporting multiple securities.

• sequitur

  • sequitur is a library that lets you create and train an autoencoder for sequential data in just two lines of code.

• sktime

  • sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks.

• TODS

  • TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.

• transdim

  • Machine learning for transportation data imputation and prediction.

• tsai

  • tsai is an open-source deep learning package built on top of Pytorch & fastai focused on state-of-the-art techniques for time series tasks like classification, regression, forecasting, imputation...

• tsam

  • tsam is a python package which uses different machine learning algorithms for the aggregation of time series.

• tsaug

  • tsaug is a Python package for time series augmentation.

• tsfresh

  • tsfresh provides systematic time-series feature extraction by combining established algorithms from statistics, time-series analysis, signal processing, and nonlinear dynamics with a robust feature selection algorithm.

• tspiral

  • A python package for time series forecasting with scikit-learn estimators.

• ADRepository: Real-world anomaly detection datasets

• Electricity Transformer Dataset (ETDataset)

• Monash Time Series Forecasting Repository

• Open Source Asset Pricing

• UCR Time Series Classification Archive

• UEA & UCR Time Series Classification Repository

• Forecasting: Principles and Practice (3rd ed)

  • Rob J Hyndman and George Athanasopoulos, 2021

  • This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly.

• awesome-time-series

  • List of state of the art papers, code, and other resources focus on time series forecasting.

• awesome_time_series_in_python

  • This curated list contains python packages for time series analysis.

• DL4Stock

  • This is the repository for the collection of deep learning in stock market prediction.

• Transformers for Time Series

  • Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series (Powered by PyTorch).

• Deep Learning and Machine Learning for Stock Predictions

  • This is for learning, studying, researching, and analyzing stock in deep learning (DL) and machine learning (ML).

• Stock-Prediction-Models

  • Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations.