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List of papers, code and experiments using deep learning for time series forecasting
| Date | Stars |
|---|---|
| 2026-07-24 | 2777 |
| 2026-07-25 | 2776 |
| 2026-07-28 | 2776 |
| 2026-07-30 | 2776 |
| 2026-07-31 | 2776 |
| 2026-08-06 | 2778 |
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# Deep Learning Time Series Forecasting [](http://makeapullrequest.com) List of state of the art papers focus on deep learning and resources, code and experiments using deep learning for time series forecasting. Classic methods vs Deep Learning methods, Competitions... ## [Table of Contents]() * [Papers](#Papers) * [Conferences](#Conferences) * [Competitions](#Competitions) * [Code](#Code) * [Theory-Resource](#Theory-Resource) * [Code Resource](#Code-Resource) * [Datasets](#Datasets) ## Papers ### 2021 - [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) - Haixu Wu, et al. - [[Code](https://github.com/thuml/Autoformer)] - [Long Range Probabilistic Forecasting in Time-Series using High Order Statistics](https://arxiv.org/pdf/2111.03394.pdf) - Prathamesh Deshpande, et al. - \[[Code](https://github.com/pratham16cse/AggForecaster)\] - [Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Networks](https://arxiv.org/pdf/2107.00894.pdf) - Maosen Li, et al. - Code not yet. - [End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series](http://proceedings.mlr.press/v139/rangapuram21a/rangapuram21a.pdf) - Syama Sundar Rangapuram, et al. - Code not yet. - [Neural basis expansion analysis with exogenous variables:Forecasting electricity prices with NBEATSx](https://arxiv.org/pdf/2104.05522.pdf) - Kin G. Olivares, et al. - [[Code](https://github.com/cchallu/nbeatsx)] - [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting](https://arxiv.org/pdf/2101.12072.pdf) **reference** - Kashif Rasul, et al. - [[Code](https://github.com/zalandoresearch/pytorch-ts)] - [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://www.researchgate.net/publication/347133536_An_Experimental_Review_on_Deep_Learning_Architectures_for_Time_Series_Forecasting) - Pedro Lara-Benítez, et al. - [[Code](https://github.com/pedrolarben/TimeSeriesForecasting-DeepLearning)] - [Long Horizon Forecasting With Temporal Point Processes](https://arxiv.org/pdf/2101.02815.pdf) - Prathamesh Deshpande, et al. - [[Code](https://github.com/pratham16cse/DualTPP)] - [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/pdf/2012.07436.pdf) `AAAI 2021` - Haoyi Zhou, et al. - [[Code](https://github.com/zhouhaoyi/Informer2020)] ### 2020 - [CHALLENGES AND APPROACHES TO TIME-SERIES FORECASTING IN DATA CENTER TELEMETRY: A SURVEY](https://arxiv.org/pdf/2101.04224.pdf) - Shruti Jadon, et al. - Code not yet. - [Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management](https://www.sciencedirect.com/science/article/abs/pii/S026840122031481X) - H.D. Nguyen, et al. - Code not yet. - [Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics](https://www.climatechange.ai/papers/neurips2020/41/paper.pdf) - Ján Drgona, et al. - Code not yet. - [MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://arxiv.org/pdf/2012.08791.pdf) - Angus Dempster, et al. - [[Code](https://github.com/angus924/minirocket)] - [Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction](https://proceedings.neurips.cc/paper/2020/file/abc99d6b9938aa86d1f30f8ee0fd169f-Paper.pdf) - Yuan Xue, et al. - Code not yet. - [Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9179030) - Castellani Andrea, et al. - ```Honda Research Institute Europe GmbH``` - Code not yet. - [Inter-Series Attention Model for COVID-19 Forecasting](https://arxiv.org/pd
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:18d3032094e2c763, topic:deep-learning, topic:pytorch, topic:tensorflow