{"id":15121660,"url":"https://github.com/alro10/deep-learning-time-series","last_synced_at":"2025-05-15T01:10:09.187Z","repository":{"id":40513809,"uuid":"203803065","full_name":"Alro10/deep-learning-time-series","owner":"Alro10","description":"List of papers, code and experiments using deep learning for time series forecasting","archived":false,"fork":false,"pushed_at":"2024-03-16T23:52:38.000Z","size":1277,"stargazers_count":2700,"open_issues_count":9,"forks_count":530,"subscribers_count":80,"default_branch":"master","last_synced_at":"2025-05-15T01:10:03.623Z","etag":null,"topics":["deep-learning","deep-neural-networks","demand-forecasting","forecasting-competitions","forecasting-models","lstm","lstm-neural-networks","prediction","python3","pytorch","recurrent-neural-networks","sales-forecasting","series-analysis","series-classification","series-forecasting","tensorflow","time-series","time-series-classification","time-series-forecasting","time-series-prediction"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Alro10.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-08-22T13:39:58.000Z","updated_at":"2025-05-12T08:03:35.000Z","dependencies_parsed_at":"2024-09-26T02:01:03.324Z","dependency_job_id":null,"html_url":"https://github.com/Alro10/deep-learning-time-series","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Alro10%2Fdeep-learning-time-series","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Alro10%2Fdeep-learning-time-series/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Alro10%2Fdeep-learning-time-series/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Alro10%2Fdeep-learning-time-series/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Alro10","download_url":"https://codeload.github.com/Alro10/deep-learning-time-series/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254254042,"owners_count":22039792,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","deep-neural-networks","demand-forecasting","forecasting-competitions","forecasting-models","lstm","lstm-neural-networks","prediction","python3","pytorch","recurrent-neural-networks","sales-forecasting","series-analysis","series-classification","series-forecasting","tensorflow","time-series","time-series-classification","time-series-forecasting","time-series-prediction"],"created_at":"2024-09-26T02:00:59.509Z","updated_at":"2025-05-15T01:10:04.167Z","avatar_url":"https://github.com/Alro10.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Deep Learning Time Series Forecasting\n[![PRsWelcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)\n\n\nList 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...\n\n## [Table of Contents]()\n\n* [Papers](#Papers)\n* [Conferences](#Conferences)\n* [Competitions](#Competitions)\n* [Code](#Code)\n* [Theory-Resource](#Theory-Resource)\n* [Code Resource](#Code-Resource)\n* [Datasets](#Datasets)\n\n\n## Papers\n\n### 2021\n\n- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008)\n\n  - Haixu Wu, et al.\n  - [[Code](https://github.com/thuml/Autoformer)]\n\n- [Long Range Probabilistic Forecasting in Time-Series using High Order Statistics](https://arxiv.org/pdf/2111.03394.pdf)\n\n  - Prathamesh Deshpande, et al.\n  - \\[[Code](https://github.com/pratham16cse/AggForecaster)\\]\n\n- [Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Networks](https://arxiv.org/pdf/2107.00894.pdf)\n\n  - Maosen Li, et al.\n  - Code not yet.\n\n- [End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series](http://proceedings.mlr.press/v139/rangapuram21a/rangapuram21a.pdf)\n\n  -  Syama Sundar Rangapuram, et al.\n  - Code not yet.\n\n- [Neural basis expansion analysis with exogenous variables:Forecasting electricity prices with NBEATSx](https://arxiv.org/pdf/2104.05522.pdf)\n\n  - Kin G. Olivares, et al.\n  - [[Code](https://github.com/cchallu/nbeatsx)]\n\n- [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting](https://arxiv.org/pdf/2101.12072.pdf) **reference**\n\n  - Kashif Rasul, et al.\n  - [[Code](https://github.com/zalandoresearch/pytorch-ts)]\n\n- [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)\n\n  - Pedro Lara-Benítez, et al.\n  - [[Code](https://github.com/pedrolarben/TimeSeriesForecasting-DeepLearning)]\n\n- [Long Horizon Forecasting With Temporal Point Processes](https://arxiv.org/pdf/2101.02815.pdf)\n\n  - Prathamesh Deshpande, et al.\n  - [[Code](https://github.com/pratham16cse/DualTPP)]\n\n- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/pdf/2012.07436.pdf) `AAAI 2021`\n\n  - Haoyi Zhou, et al.\n  - [[Code](https://github.com/zhouhaoyi/Informer2020)]\n\n### 2020\n\n- [CHALLENGES AND APPROACHES TO TIME-SERIES FORECASTING IN DATA CENTER TELEMETRY: A SURVEY](https://arxiv.org/pdf/2101.04224.pdf)\n\n  - Shruti Jadon, et al.\n  - Code not yet.\n\n- [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)\n\n  - H.D. Nguyen, et al.\n  - Code not yet.\n\n- [Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics](https://www.climatechange.ai/papers/neurips2020/41/paper.pdf)\n\n  - Ján Drgona, et al.\n  - Code not yet.\n\n- [MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://arxiv.org/pdf/2012.08791.pdf)\n\n  - Angus Dempster, et al.\n  - [[Code](https://github.com/angus924/minirocket)]\n\n- [Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction](https://proceedings.neurips.cc/paper/2020/file/abc99d6b9938aa86d1f30f8ee0fd169f-Paper.pdf)\n\n  - Yuan Xue, et al.\n  - Code not yet.\n\n- [Real-World Anomaly Detection by using Digital\nTwin Systems and Weakly-Supervised Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9179030)\n\n  - Castellani Andrea, et al.\n  - ```Honda Research Institute Europe GmbH```\n  - Code not yet.\n\n- [Inter-Series Attention Model for COVID-19 Forecasting](https://arxiv.org/pdf/2010.13006.pdf) **Good reference**\n\n  - Xiaoyong Jin, et al.\n  - [[Code](https://github.com/Gandor26/covid-open)]\n\n- [MODEL SELECTION IN RECONCILING HIERARCHICAL TIME SERIES](https://arxiv.org/pdf/2010.10742.pdf)\n\n  - M. ABOLGHASEMI, et al.\n  - [[Code](https://github.com/mahdiabolghasemi/Conditional-reconciliation-in-HF)]\n\n- [A Strong Baseline for Weekly Time Series Forecasting](https://arxiv.org/pdf/2010.08158.pdf)\n\n  - Rakshitha Godahewa, et al.\n  - [[Code](https://github.com/rakshitha123/WeeklyForecasting)]\n\n- [Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning](https://arxiv.org/pdf/2010.05783.pdf)\n\n  - Trey McNeely, et al.\n  - Code not yet.\n\n- [Modeling Heterogeneous Seasonality With Recurrent Neural Networks Using IoT Time Series Data for Defrost Detection and Anomaly Analysis](https://dash.harvard.edu/bitstream/handle/1/37365638/KHETARPAL-DOCUMENT-2020.pdf?sequence=1\u0026isAllowed=y) **Good Reference**\n\n  - Khetarpal, Suraj.\n  - Code not yet.\n\n- [An Examination of the State-of-the-Art for Multivariate Time Series Classification](https://www.researchgate.net/profile/Georgiana_Ifrim/publication/344501445_An_Examination_of_the_State-of-the-Art_for_Multivariate_Time_Series_Classification/links/5f7cdfb2458515b7cf6c4efd/An-Examination-of-the-State-of-the-Art-for-Multivariate-Time-Series-Classification.pdf)\n\n  - Bhaskar Dhariyal, et al.\n  - Code noy yet.\n\n- [Rank Position Forecasting in Car Racing](https://arxiv.org/pdf/2010.01707.pdf)\n\n  - Bo Peng, et al.\n  - Code not yet.\n\n- [Mixed Membership Recurrent Neural Networks for Modeling Customer Purchases](http://www.columbia.edu/~jwp2128/Papers/FazelniaIbrahimetal2020.pdf)\n\n  - Ghazal Fazelnia, et al.\n  - Code not yet.\n\n- [An analysis of deep neural networks for predicting trends in time series data](https://arxiv.org/pdf/2009.07943.pdf)\n\n  - Kouame Kouassi and Deshendran Moodley.\n  - Code not yet.\n\n- [Automatic Forecasting using Gaussian Processes](https://arxiv.org/pdf/2009.08102.pdf)\n\n  - G. Corani\n  - Code not yet.\n\n- [Attention based Multi-Modal New Product Sales Time-series Forecasting](https://dl.acm.org/doi/10.1145/3394486.3403362)\n\n  - Vijay Ekambaram\n  - Code not yet.\n\n- [Demand Forecasting of individual Probability Density Functions with Machine Learning](https://arxiv.org/pdf/2009.07052.pdf)\n\n  - Felix Wick, et al.\n  - Code not yet.\n\n- [A Time-Series Forecasting Performance Comparison for Neural Networks with State Space and ARIMA Models](http://www.ieomsociety.org/detroit2020/papers/37.pdf)\n\n   - Milton Soto-Ferrari\n   - Code not yet.\n\n- [Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature](https://www.researchgate.net/profile/Karthick_Thiyagarajan7/publication/344199272_Short-term_Time_Series_Forecasting_of_Concrete_Sewer_Pipe_Surface_Temperature/links/5f5b0b4492851c07895d48fc/Short-term-Time-Series-Forecasting-of-Concrete-Sewer-Pipe-Surface-Temperature.pdf)\n\n  - Karthick Thiyagarajan, et al.\n  - Code not yet.\n\n- [Multivariate Time-series Anomaly Detection via Graph Attention Network](https://arxiv.org/pdf/2009.02040.pdf)\n\n  - Hang Zhao, et al.\n  - Code not yet.\n\n- [Graph Neural Networks for Model Recommendation using Time Series Data](https://arxiv.org/pdf/2009.03474.pdf)\n\n  - Aleksandr Pletnev, et al.\n  - Code not yet.\n\n- [Kaggle forecasting competitions: An overlooked learning opportunity](https://www.sciencedirect.com/science/article/pii/S0169207020301114)\n\n  - Casper Solheim Bojer and Jens Peder Meldgaard.\n  - [[Code](https://github.com/cbojer/kaggle-project)]\n\n- [Forecasting with Multiple Seasonality](https://arxiv.org/pdf/2008.12340.pdf)\n\n  - Tianyang Xie and Jie Ding.\n  - Code not yet.\n\n- [LAVARNET: Neural network modeling of causal variable relationships for multivariate time series forecasting](https://arxiv.org/pdf/2009.00945.pdf)\n\n  - Christos Koutlis, et al.\n  - Code not yet.\n\n- [Forecasting Hierarchical Time Series with a Regularized Embedding Space](https://kdd-milets.github.io/milets2020/papers/MiLeTS2020_paper_13.pdf)\n\n  - Jeffrey L. Gleason.\n  - [[Code](https://github.com/jlgleason/hts-constrained-embeddings)]\n\n- [Forecasting the Evolution of Hydropower Generation](https://dl.acm.org/doi/abs/10.1145/3394486.3403337)\n\n  - Fan Zhou, et al.\n  - [[Code](https://github.com/Anewnoob/DeepHydro)]\n\n- [Deep State-Space Generative Model For Correlated Time-to-Event Predictions](https://dl.acm.org/doi/abs/10.1145/3394486.3403206)\n\n  - Yuan Xue, et al.\n  - Code not yet.\n\n- [Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries](https://mpra.ub.uni-muenchen.de/102315/1/MPRA_paper_102315.pdf)\n\n  - Fantazzini, Dean.\n  - Code not yet.\n\n- [Scalable Low-Rank Autoregressive Tensor Learning for Spatiotemporal Traffic Data Imputation](https://arxiv.org/pdf/2008.03194.pdf)\n\n  - Xinyu Chen, et al.\n  - [[Code](https://github.com/xinychen/tensor-learning)]\n\n- [clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series](https://www.vanderschaar-lab.com/papers/2020_Clairvoyance.pdf)\n\n  - Daniel Jarrett, et al.\n  - Code not yet.\n\n- [Speed Anomalies and Safe Departure Times from Uber Movement Data](http://urban.cs.wpi.edu/urbcomp2020/file/08.pdf)\n\n  - Nabil Al Nahin Ch, et al.\n  - Code not yet.\n\n- [Forecasting AI Progress: A Research Agenda](https://arxiv.org/pdf/2008.01848.pdf)\n\n  - Ross Gruetzemacher, et al.\n  - Review\n\n- [Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation](https://arxiv.org/abs/2008.02663)\n\n  - Kasun Bandara, et al.\n  - Code not yet.\n\n- [Interpretable Sequence Learning for COVID-19 Forecasting](https://arxiv.org/pdf/2008.00646.pdf)\n\n  - Sercan O. Arık, et al.\n  - [[Code](https://github.com/reichlab/covid19-forecast-hub)]\n\n- [Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records](https://arxiv.org/pdf/2008.00181.pdf) **meta-learning**\n\n  - Jiatu Shi, et al.\n  - Code not yet.\n\n- [Forecasting Economic Recession through Share Price in the Logistics Industry with Artificial Intelligence (AI)](https://www.mdpi.com/2079-3197/8/3/70/pdf)\n\n  - YM Tang, et al.\n  - Code not yet.\n\n- [PRINCIPLES AND ALGORITHMS FOR FORECASTING GROUPS OF TIME SERIES: LOCALITY AND GLOBALITY](https://arxiv.org/pdf/2008.00444.pdf)\n\n  - Pablo Montero-Manso and Rob J Hyndman\n  - Code not yet.\n\n- [Multi-stream RNN for Merchant Transaction Prediction](https://arxiv.org/pdf/2008.01670.pdf)\n\n  - Zhongfang Zhuang, et al.\n  - `KDD 2020 Workshop on Machine Learning in Finance`\n  - Code not yet.\n\n- [Prediction of hierarchical time series using structured regularization and its application to artificial neural networks](https://arxiv.org/pdf/2007.15159.pdf)\n\n  - Tomokaze Shiratori, et al.\n  - Code not yet.\n\n- [Cold-Start Promotional Sales Forecasting through Gradient Boosted-based Contrastive Explanations](https://ieeexplore.ieee.org/abstract/document/9149573)\n\n  - Carlos Aguilar-Palacios, et al.\n  - [[Code](https://github.com/CarlitosDev/contrastiveExplanation/tree/master/contrastiveRegressor)]\n\n- [Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models](https://arxiv.org/pdf/2007.15541.pdf)\n\n  - Fadhel Ayed, et al.\n  - `Amazon Research`\n  - [[Code](https://github.com/awslabs/gluon-ts/tree/distribution_anomaly_detection/distribution_anomaly_detection)]\n\n- [Demand Forecasting in the Presence of Privileged Information](https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/ariannezhad-2020-demand.pdf)\n\n  - Mozhdeh Ariannezhad, et al.\n  - [[Code](https://github.com/mzhariann/PIANN)]\n\n- [Seasonal Self-evolving Neural Networks Based Short-term Wind Farm Generation Forecast](https://www.researchgate.net/publication/342976923_Seasonal_Self-evolving_Neural_Networks_Based_Short-term_Wind_Farm_Generation_Forecast)\n\n  - Yunchuan Liu, et al.\n  - Code not yet.\n\n- [Distributed ARIMA Models for Ultra-long Time Series](https://arxiv.org/pdf/2007.09577.pdf) **Spark**\n\n  - Xiaoqian Wang, et al.\n  - [[Code](https://github.com/xqnwang/darima)]\n\n- [Adversarial Attacks on Probabilistic Autoregressive Forecasting Models](https://arxiv.org/abs/2003.03778)\n\n  - Raphaël Dang-Nhu, et al.\n  - [[Code](https://github.com/eth-sri/probabilistic-forecasts-attacks)]\n\n- [Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction](https://arxiv.org/pdf/2007.03568.pdf) **LSTM application**\n\n  - Alexander Acker, et al.\n  - [[Code](https://github.com/citlab/fed_challenge)]\n\n- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://arxiv.org/pdf/2007.02842.pdf)\n\n  - Lei Bai, et al.\n  - [[Code](https://github.com/LeiBAI/AGCRN)]\n\n- [Dynamic Multi-Scale Convolutional Neural Network for Time Series Classification](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9115645)\n\n  - BIN QIAN, et al.\n  - Code not yet.\n\n- [Neural Architecture Search for Time Series Classification](https://germain-forestier.info/publis/ijcnn2020.pdf)\n\n  - Hojjat Rakhshani, et al.\n  - [[Code](https://github.com/ML-MHs/IJCNN2020)]\n\n- [Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions](https://arxiv.org/pdf/2006.13707.pdf)\n\n  - Ahmed M. Alaa and Mihaela van der Schaar.\n  - Code not yet.\n\n- [Time Series Regression](https://arxiv.org/pdf/2006.12672.pdf)\n\n  - Chang Wei Tan, et al.\n  - [[Code](https://github.com/ChangWeiTan/TSRegression)]\n\n- [Forecasting Supplier Delivery Performance with Recurrent Neural Networks](https://odr.chalmers.se/bitstream/20.500.12380/300824/1/Master_s_Thesis_Johan_Ramne_.pdf)\n\n  - Johan Ramne\n  - Master Thesis.\n\n- [Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments](https://arxiv.org/pdf/2006.10119.pdf)\n\n  - Fatih Ilhan, et al.\n  - Code not yet.\n\n- [Resilient Neural Forecasting Systems](https://dl.acm.org/doi/pdf/10.1145/3399579.3399869)\n\n  - Michael Bohlke-Schneider, et al.\n  - `Amazon Research`\n  - Code not yet.\n\n- [Dynamic Neural Relational Inference for Forecasting Trajectories](http://openaccess.thecvf.com/content_CVPRW_2020/papers/w66/Graber_Dynamic_Neural_Relational_Inference_for_Forecasting_Trajectories_CVPRW_2020_paper.pdf)\n\n  - Colin Graber and Alexander Schwing\n  - `CVPR 2020`\n  - [[Code](https://github.com/cgraber/cvpr_dNRI)]\n\n- [Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting](https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12644)\n\n  - Ling Cai, et al.\n  - Code not yet.\n\n- [Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series](https://arxiv.org/pdf/2006.06553.pdf)\n\n   - Anna K. Yanchenko and Sayan Mukherjee.\n   - Code not yet.\n\n- [Neuroevolution Strategy for Time Series Prediction](https://www.scirp.org/journal/paperinformation.aspx?paperid=100727)\n\n   - George Naskos, et al.\n   - Code not yet.\n\n- [COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population](https://www.mdpi.com/2076-3417/10/11/3880)\n\n  - Vasilis Papastefanopoulos, et al.\n  - [[Code](https://github.com/ML-Upatras/COVID-19-A-comparison-of-time-series-methods-foractive-cases-forecasting)]\n\n- [A machine learning approach for forecasting hierarchical time series](https://arxiv.org/pdf/2006.00630.pdf)\n\n  - Paolo Mancuso, et al.\n  - Code not yet.\n\n- [ProbCast: Open-source Production, Evaluation and Visualisation of Probabilistic Forecasts](http://www.jethrobrowell.com/uploads/4/5/4/0/45405281/probcast___pmaps2020.pdf)\n\n  - Jethro Browell and Ciaran Gilbert.\n  - [[Code](https://github.com/jbrowell/ProbCast)]\n\n- [Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Models](https://ecole-itn.eu/wp-content/uploads/2020/05/IJCNN2020_Sibghat_Final.pdf)**meta-learning**\n\n  - Sibghat Ullah, et al.\n  - [[Code](https://github.com/SibghatUllah13/VRNNs-for-Clinical-Time-Series-Forecasting)]\n\n- [Semisupervised Deep State-Space Model for Plant Growth Modeling](https://spj.sciencemag.org/plantphenomics/2020/4261965/)\n\n  - S. Shibata, et al.\n  - Code not yet.\n\n- [EFFECTIVE AND EFFICIENT COMPUTATION WITH MULTIPLE-TIMESCALE SPIKING RECURRENT NEURAL NETWORKS](https://arxiv.org/pdf/2005.11633.pdf)\n\n  - Bojian Yin, et al.\n  - Code not yet.\n\n- [Multivariate time series forecasting via attention-based encoder–decoder framework](https://www.sciencedirect.com/science/article/abs/pii/S0925231220300606)\n\n  - Shengdong Du, et al.\n  - `Neurocomputing`\n  - Code not yet.\n\n- [A Novel LSTM for Multivariate Time Series with Massive Missingness](https://www.mdpi.com/1424-8220/20/10/2832)\n\n  - Nazanin Fouladgar and Kary Främling.\n  - Code not yet.\n\n- [N-BEATS: NEURAL BASIS EXPANSION ANALYSIS FOR INTERPRETABLE TIME SERIES FORECASTING](https://arxiv.org/pdf/1905.10437.pdf)` ICLR 2020`\n\n  - Boris N. Oreshkin, et al.\n  - Code not yet.\n\n- [How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting](https://arxiv.org/pdf/2005.10698.pdf)**good new approach**\n\n  - Robin Hirt, et al.\n  - Code not yet.\n\n- [The Hybrid Forecasting Method SVR-ESAR for Covid-19](https://www.medrxiv.org/content/medrxiv/early/2020/05/22/2020.05.20.20103200.full.pdf)\n\n  - Juan Frausto Solis, et al.\n  - Code not yet.\n\n- [Forecasting the Short-Term Metro Ridership With Seasonal and Trend Decomposition Using Loess and LSTM Neural Networks](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9094173)\n\n  - DEWANG CHEN, et al.\n  - Code not yet.\n\n- [The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models](https://arxiv.org/pdf/2005.10111.pdf)\n\n  - Stephan Rabanser, et al.\n  - `AWS AI Labs`\n  - Code not yet.\n\n- [FORECASTING WITH SKTIME: DESIGNING SKTIME’S NEW FORECASTING API AND APPLYING IT TO REPLICATE AND EXTEND THE M4 STUDY](https://arxiv.org/pdf/2005.08067.pdf)\n\n  - Markus Löning and Franz J. Király.\n  - [[Code](https://github.com/sktime/sktime-dl)]\n\n- [LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns](https://arxiv.org/pdf/1909.04293.pdf)\n\n  - Kasun Bandara, et al.\n  - [[Code](https://github.com/kasungayan/LSTMMSNet)]\n\n- [A NETWORK-BASED TRANSFER LEARNING APPROACH TO IMPROVE SALES FORECASTING OF NEW PRODUCTS](https://arxiv.org/pdf/2005.06978.pdf)\n\n  - Karb, Tristan, et al.\n  - Code not yet.\n\n- [DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting](https://dl.acm.org/doi/10.1145/3357384.3358132) **Good new approach**\n\n  - Siteng Huang, et al.\n  - [[Code](https://github.com/bighuang624/DSANet)]\n\n- [An Approach for Complex Event Streams Processing and Forecasting](https://fruct.org/publications/fruct26/files/Moro.pdf)\n\n  - Viktor Morozov, Mikhail Petrovskiy.\n  - Code not yet.\n\n- [Knowledge Enhanced Neural Fashion Trend Forecasting](https://arxiv.org/pdf/2005.03297.pdf)\n\n  - Yunshan Ma, et al.\n  - Code not yet.\n\n- [Augmented Out-of-Sample Comparison Method for Time Series Forecasting Techniques](https://link.springer.com/chapter/10.1007/978-3-030-47358-7_30)\n\n  - Igor Ilic, et al.\n  - Code not yet.\n\n- [Enhancing High Frequency Technical Indicators Forecasting Using Shrinking Deep Neural Networks](https://ieeexplore.ieee.org/abstract/document/9081393) `ICIM 2020`\n\n  - Xiaoyu Tan, et al.\n  - Code not yet.\n\n- [Time Series Forecasting With Deep Learning: A Survey](https://arxiv.org/pdf/2004.13408.pdf) **Good summary**\n\n  - Bryan Lim and Stefan Zohren\n  - Survey\n\n- [Neural forecasting: Introduction and literature overview](https://arxiv.org/pdf/2004.10240.pdf)\n\n   - Konstantinos Benidis, et al.\n   - Not is a overview.\n\n- [Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories](https://arxiv.org/pdf/2004.09760.pdf)\n\n   - Hao Xue, et al.\n   - Code not yet.\n\n- [Orbit: Probabilistic Forecast with Exponential Smoothing](https://arxiv.org/pdf/2004.08492.pdf)\n\n   - Edwin Ng, et a.\n   - Code is available upon request.\n\n- [Daily retail demand forecasting using machine learning with emphasis on calendric special days](https://www.sciencedirect.com/science/article/abs/pii/S0169207020300224)\n\n   - Jakob Huber and Heiner Stuckenschmidt.\n   - Code not yet.\n\n- [FORECASTING IN MULTIVARIATE IRREGULARLY SAMPLED TIME SERIES WITH MISSING VALUES](https://arxiv.org/pdf/2004.03398.pdf)\n\n   - Shivam Srivastava, et al.\n   - Code not yet.\n   - **IBM Almaden Research Center**.\n\n- [Multi-label Prediction in Time Series Data using Deep Neural Networks](https://arxiv.org/pdf/2001.10098.pdf)\n\n   - Wenyu Zhang, et al.\n   - Code not yet.\n\n- [TraDE: Transformers for Density Estimation](https://arxiv.org/pdf/2004.02441.pdf)\n\n   - Rasool Fakoor, et al.\n   - Code not yet.\n\n- [Deep Probabilistic Modelling of Price Movements for High-Frequency Trading](https://arxiv.org/pdf/2004.01498.pdf)\n\n   - Ye-Sheen Lim and Denise Gorse.\n   - Code not yet.\n\n- [Deep State Space Models for Nonlinear System Identification](https://arxiv.org/pdf/2003.14162.pdf)\n\n   - Daniel Gedon, et al.\n   - Code not yet.\n\n- [Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks](https://arxiv.org/pdf/2003.12162.pdf)\n\n   - Bernardo Perez Orozco and  Stephen J. Roberts.\n   - [[Code](https://github.com/bperezorozco/ordinal_tsf)]\n\n- [Financial Time Series Representation Learning](https://arxiv.org/pdf/2003.12194.pdf)\n\n   - Philippe Chatigny, et al.\n   - Code not yet.\n\n- [G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes](https://arxiv.org/pdf/2003.10551.pdf)\n\n  - Rui Li, et al.\n  - - ```IBM research and MIT```\n  - Code not yet.\n\n- [Deep Markov Spatio-Temporal Factorization](https://arxiv.org/pdf/2003.09779.pdf)\n\n  - Amirreza Farnoosh, et al.\n  - Code not yet.\n\n- [Harmonic Recurrent Process for Time Series Forecasting](https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/ecai20hr.pdf)\n\n  - Shao-Qun Zhang and Zhi-Hua Zhou.\n  - Code not yet.\n\n- [Elastic Machine Learning Algorithms in Amazon SageMaker](https://ssc.io/pdf/modin711s.pdf)\n\n  - Edo Liberty, et al.\n  - Code not yet.\n\n- [Time Series Data Augmentation for Deep Learning: A Survey](https://arxiv.org/pdf/2002.12478.pdf)\n\n  - Qingsong Wen, et al.\n  - Code not yet.\n\n- [Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting](https://arxiv.org/pdf/2002.12135.pdf)```AAAI 2020```**meta-learning**\n\n  - QIQUAN SHI, et al.\n  - [[Code](https://github.com/huawei-noah/BHT-ARIMA)]\n\n- [Learnings from Kaggle's Forecasting Competitions](https://www.researchgate.net/publication/339362837_Learnings_from_Kaggle's_Forecasting_Competitions)\n\n  - Casper Solheim Bojer, et al.\n  - Code not yet.\n\n- [An Industry Case of Large-Scale Demand Forecasting of Hierarchical Components](https://ieeexplore.ieee.org/abstract/document/8999262)\n\n  - Rodrigo Rivera-Castro, et al.\n  - Code not yet.\n\n- [Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows](https://arxiv.org/pdf/2002.06103.pdf)\n\n  - Kashif Rasul, et al.\n  - [[Code](https://github.com/zalandoresearch/pytorch-ts)].\n\n- [ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting](https://arxiv.org/pdf/2002.04155.pdf)\n\n  - Joel Janek Dabrowski, et al.\n  - Code not yet.\n\n- [Anomaly detection for Cybersecurity: time series forecasting and deep learning](https://pdfs.semanticscholar.org/810b/dfa0f63f03473be79556b90dc79a88a1f769.pdf)`Good review about forecasting`\n\n  - Giordano Colò.\n  - Code not yet.\n\n- [Event-Driven Continuous Time Bayesian Networks](https://krvarshney.github.io/pubs/BhattacharjyaSGMVS_aaai2020.pdf)\n\n  - Debarun Bhattacharjya, et al.\n  - `Research AI, IBM`\n  - Code not yet.\n\n- [Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values](https://arxiv.org/pdf/1911.10273.pdf)```AAAI 2020```\n\n  - Xianfeng Tang, et al.\n  - ```IBM Research, NY```\n  - Code not yet.\n\n- [Topology-Based Clusterwise Regression for User Segmentation and Demand Forecasting](https://ieeexplore.ieee.org/abstract/document/8964133)\n\n  - Rodrigo Rivera-Castro, et al.\n  - Code not yet.\n\n- [Evolutionary LSTM-FCN networks for pattern classification in industrial processes](https://www.sciencedirect.com/science/article/abs/pii/S2210650219301270)\n\n  - Patxi Ortego, et al.\n  - Code not yet.\n\n- [Forecasting Multivariate Time-Series Data Using LSTM and Mini-Batches](https://rd.springer.com/chapter/10.1007/978-3-030-37309-2_10)\n\n  - Athar Khodabakhsh, et al.\n  - Code not yet.\n\n- [Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series](https://faculty.ist.psu.edu/xzz89/publications/AAAI20.pdf)```AAAI 2020```\n\n  - Dongkuan Xu, et al.\n  - [[Code](https://github.com/DerronXu/DeepTrends/tree/master)]\n\n- **[RELATIONAL STATE-SPACE MODEL FOR STOCHASTIC MULTI-OBJECT SYSTEMS](https://arxiv.org/pdf/2001.04050.pdf)**```ICLR 2020```\n\n  - Fan Yang, et al.\n  - Code not yet.\n\n- [For2For: Learning to forecast from forecasts](https://arxiv.org/pdf/2001.04601.pdf)\n\n  - Zhao, Shi, et al.\n  - Code not yet.\n\n- [Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning](https://arxiv.org/pdf/1909.08181.pdf) `AAAI 2020`\n\n  - Long H. Nguyen, et al.\n  - Code not yet\n\n### 2019\n\n- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://www.aminer.cn/pub/5db9297a47c8f766461f7974/enhancing-the-locality-and-breaking-the-memory-bottleneck-of-transformer-on-time?anchor=conclusion) **Reference**\n\n  - Shiyang Li, et al.\n  - [[Code](https://github.com/mlpotter/Transformer_Time_Series)]\n\n- [Forecasting Big Time Series: Theory and Practice](https://dl.acm.org/doi/pdf/10.1145/3292500.3332289)`KDD 2019` **Relevant tutorial**\n\n  - Christos Faloutsos, et al.\n  - [[Code](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/)]\n\n- [Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting](http://urban-computing.com/pdf/kdd19-BinWang.pdf)\n\n  - Bin Wang, et al.\n  - [[Code](https://github.com/BruceBinBoxing/Deep_Learning_Weather_Forecasting)]\n\n- [A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting](https://www.researchgate.net/publication/334556784_A_hybrid_method_of_exponential_smoothing_and_recurrent_neural_networks_for_time_series_forecasting)\n\n  - Slawek Smyl\n  - `Winning submission of the M4 forecasting competition`\n  - [[Code](https://github.com/Mcompetitions/M4-methods/tree/slaweks_ES-RNN/118%20-%20slaweks17)]\n\n- [Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting](https://arxiv.org/pdf/1905.03806.pdf)`NeurIPS 2019`\n\n  - Rajat Sen, et al.\n  - `Amazon`\n  - [[Code](https://github.com/rajatsen91/deepglo)]\n\n- [Deep Landscape Forecasting for Real-time Bidding Advertising](https://arxiv.org/abs/1905.03028) `KDD 2019`\n\n  - Kan Ren, et al.\n  - [[Code](https://github.com/rk2900/DLF)]\n\n- [Similarity Preserving Representation Learning for Time Series Clustering](https://arxiv.org/pdf/1702.03584.pdf)\n\n  - Qi Lei, et al.\n  - ```IBM research```\n  - [[Code](https://github.com/cecilialeiqi/SPIRAL)]\n\n- [DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting](https://dl.acm.org/doi/abs/10.1145/3357384.3358132)\n\n  - Siteng Huang, et al.\n  - Code not yet.\n\n- [Enhancing Time Series Momentum Strategies Using Deep Neural Networks](https://arxiv.org/pdf/1904.04912.pdf)\n\n  - Bryan Lim, et al.\n  - Code not yet.\n\n- [DYNAMIC TIME LAG REGRESSION: PREDICTING WHAT \u0026 WHEN](https://hal.inria.fr/hal-02422148/document)\n\n  - Mandar Chandorkar, et al.\n  - Code not yet.\n\n- [Time-series Generative Adversarial Networks](https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf)`NeurIPS 2019`\n\n  - Jinsung Yoon. et al.\n  - Code not yet.\n\n- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://arxiv.org/pdf/1912.09363.pdf)\n\n  - Bryan Lim, et al.\n  - ```Google Research```\n  - [[Code](https://github.com/google-research/google-research/tree/master/tft)]\n\n\n- [Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models](https://openreview.net/pdf?id=H1xXYy3VKr)\n\n  - Vincent Fortuin, et al.\n  - Code not yet.\n\n- [Deep Physiological State Space Model for Clinical Forecasting](https://arxiv.org/pdf/1912.01762.pdf)\n\n  - Yuan Xue, et al.\n  - not yet\n\n- [AR-Net: A simple Auto-Regressive Neural Network for time-series](https://arxiv.org/abs/1911.12436)\n\n  - Oskar Triebe, et al.\n  -  ```Facebook Research```\n  - Code not yet.\n\n- [Learning Time-series Data of Industrial Design Optimization using Recurrent Neural Networks](https://ecole-itn.eu/wp-content/uploads/2019/11/LMID_Sneha_finalversion.pdf)\n\n  - Sneha Saha, et al.\n  - ```Honda Research Institute Europe GmbH```\n  - Code not yet.\n\n\n- [RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series](https://arxiv.org/abs/1812.01767)\n\n  - Qingsong Wen, et al.\n  - [[Code](https://github.com/LeeDoYup/RobustSTL)]\n\n- [Constructing Gradient Controllable Recurrent Neural Networks Using Hamiltonian Dynamics](https://arxiv.org/pdf/1911.05035.pdf)\n\n  - Konstantin Rusch, et al.\n  - Code not yet.\n\n- [SOM-VAE: Interpretable Discrete Representation Learning on Time Series](https://openreview.net/pdf?id=rygjcsR9Y7)`ICLR 2019`\n\n  - Vincent Fortuin, et al.\n  - [[Code](https://github.com/ratschlab/SOM-VAE)]\n\n- [Unsupervised Scalable Representation Learning for Multivariate Time Series](https://arxiv.org/abs/1901.10738)`NeurIPS 2019`  [In Applications -- Time Series Analysis ](https://nips.cc/Conferences/2019/Schedule?showParentSession=15627)\n\n  - Jean-Yves Franceschi, et al.\n  - [[Code](https://github.com/White-Link/UnsupervisedScalableRepresentationLearningTimeSeries)]\n\n- [Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series](https://arxiv.org/pdf/1905.13570v1.pdf)\n\n  - Zhi-Xuan Tan, et al.\n  - Code not yet.\n\n- [You May Not Need Order in Time Series Forecasting](https://arxiv.org/pdf/1910.09620.pdf)\n\n  - Yunkai Zhang, et al.\n  - Code not yet\n\n  \u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/Alro10/deep-learning-time-series/blob/master/YMN_order.png\" alt=\"alt text\" width=\"80%\" height=\"60%\"\u003e\n  \u003c/p\u003e\n\n- [Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models](https://arxiv.org/pdf/1909.09020.pdf)`NeurIPS2019`\n\n  - Vincent Le Guen and Nicolas Thome.\n  - [[Code](https://github.com/vincent-leguen/STDL)]\n\n- [Dynamic Local Regret for Non-convex Online Forecasting](https://arxiv.org/pdf/1910.07927.pdf)`NeurIPS 2019`\n\n  - Sergul Aydore, et al.\n  - [[Code](https://github.com/Timbasa/Dynamic_Local_Regret_for_Non-convex_Online_Forecasting_NeurIPS2019)]\n\n- [Bayesian Temporal Factorization for Multidimensional Time Series Prediction](https://arxiv.org/pdf/1910.06366.pdf)\n\n  - Xinyu Chen, and Lijun Sun\n  - [[Code and data](https://github.com/xinychen/transdim)]\n\n- [Probabilistic sequential matrix factorization](https://arxiv.org/pdf/1910.03906.pdf)\n\n  - Ömer Deniz Akyildiz, et al.\n  - Code not yet\n\n- [Sequential VAE-LSTM for Anomaly Detection on Time Series](https://arxiv.org/pdf/1910.03818.pdf)\n\n  - Run-Qing Chen, et al.\n  - Code not yet\n\n- [High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes](https://arxiv.org/pdf/1910.03002.pdf)`NeurIPS 2019`\n\n  - David Salinas, et al.\n  - Code not yet\n\n- [Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction](https://arxiv.org/pdf/1901.08096.pdf)\n\n  - Bryan Lim, et al.\n  - Code not yet\n\n- [LHCnn: A Novel Efficient Multivariate Time Series Prediction Framework Utilizing Convolutional Neural Networks](https://ieeexplore.ieee.org/abstract/document/8855402)\n\n  - Chengxi Liu, et al.\n  - Code not yet\n\n- [SKTIME: A UNIFIED INTERFACE FOR MACHINE LEARNING WITH TIME SERIE](https://arxiv.org/pdf/1909.07872.pdf)\n\n  - [[Code](https://github.com/alan-turing-institute/sktime)]\n\n\n- **[Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://arxiv.org/pdf/1909.00590.pdf)**\n\n  - [[Code](https://github.com/HansikaPH/time-series-forecasting)]\n\n- [Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model](https://www.researchgate.net/profile/Antonio_Parmezan/publication/330742498_Evaluation_of_statistical_and_machine_learning_models_for_time_series_prediction_Identifying_the_state-of-the-art_and_the_best_conditions_for_the_use_of_each_model/links/5c558145a6fdccd6b5dc3e2e/Evaluation-of-statistical-and-machine-learning-models-for-time-series-prediction-Identifying-the-state-of-the-art-and-the-best-conditions-for-the-use-of-each-model.pdf)\n\n   - Antonio Rafael Sabino Parmezan, Vinicius M. A. Souza and Gustavo E. A. P. A. Batista. USP\n\n\n- [Explainable Deep Neural Networks for Multivariate Time Series Predictions](https://www.ijcai.org/proceedings/2019/0932.pdf) `IJCAI 2019`\n\n  - Roy Assaf and Anika Schumann.\n  - ```IBM Research, Zurich```\n  - Code not yet\n\n\n- [Outlier Detection for Time Series with Recurrent Autoencoder Ensembles](https://www.ijcai.org/proceedings/2019/0378.pdf) `IJCAI 2019`\n\n  - [[Code](https://github.com/tungk/OED)]\n\n- [Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting](https://www.ijcai.org/proceedings/2019/0402.pdf) `IJCAI 2019`\n\n  - Code not yet\n\n- [Deep Factors for Forecasting](https://arxiv.org/pdf/1905.12417.pdf) `ICML 2019`\n\n  - Yuyang Wang, et al.\n  - Code not yet\n\n- [Probabilistic Forecasting with Spline Quantile Function RNNs](http://proceedings.mlr.press/v89/gasthaus19a/gasthaus19a.pdf)\n\n  - Code not yet\n\n- [Deep learning for time series classification: a review](https://arxiv.org/abs/1809.04356)\n\n  - Code not yet\n\n- [Multivariate LSTM-FCNs for Time Series Classification](https://arxiv.org/abs/1801.04503)\n\n  - Code not yet\n\n- [Criteria for classifying forecasting methods](https://www.sciencedirect.com/science/article/pii/S0169207019301529)\n\n  - Code not yet\n\n- [GluonTS: Probabilistic Time Series Models in Python](https://arxiv.org/abs/1906.05264)\n\n   - [[Code](https://gluon-ts.mxnet.io)]\n\n- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://arxiv.org/abs/1704.04110)\n\n  - David Salinas, et al.\n  - Code not yet\n\n### 2018\n\n- [An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting](https://arxiv.org/pdf/1705.04378.pdf)\n\n  - Filippo Maria Bianchi, et al.\n  - Code not yet.\n\n- [Statistical and Machine Learning forecasting methods: Concerns and ways forward](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0194889)\n\n  - Spyros Makridakis, et al.\n  - Code not yet.\n\n- [Attend and Diagnose: Clinical Time Series Analysis Using Attention Models](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/16325/16790) `AAAI 2018`\n\n  - Huan Song, Deepta Rajan, et al.\n  - not yet.\n\n- [Precision and Recall for Time Series](http://papers.nips.cc/paper/7462-precision-and-recall-for-time-series) `NeurIPS2018`\n\n  - Nesime Tatbul, et al.\n  - Code not yet.\n\n- [Deep State Space Models for Time Series Forecasting](https://papers.nips.cc/paper/8004-deep-state-space-models-for-time-series-forecasting.pdf) `NeurIPS2018`\n\n  - Code not yet\n\n- [Deep Factors with Gaussian Processes for Forecasting](https://arxiv.org/abs/1812.00098)\n\n  - `Third workshop on Bayesian Deep Learning (NeurIPS 2018)`\n  - [[Code](https://aws.amazon.com/blogs/machine-learning/now-available-in-amazon-sagemaker-deepar-algorithm-for-more-accurate-time-series-forecasting/)]\n\n- [DIFFUSION CONVOLUTIONAL RECURRENT NEURAL NETWORK: DATA-DRIVEN TRAFFIC FORECASTING](https://arxiv.org/pdf/1707.01926.pdf)`ICLR 2018`\n\n  - Yaguang Li, et al.\n  - [[Code](https://github.com/liyaguang/DCRNN)]\n\n- [DEEP TEMPORAL CLUSTERING: FULLY UNSUPERVISED LEARNING OF TIME-DOMAIN FEATURES](https://arxiv.org/pdf/1802.01059.pdf)\n\n  - Naveen Sai Madiraju, et al.\n  - [[Code-unofficial implementation ](https://github.com/FlorentF9/DeepTemporalClustering)]\n\n- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://arxiv.org/pdf/1703.07015.pdf)\n\n  - Guokun Lai, Wei-Cheng Chang, Yiming Yang, Hanxiao Liu\n  - [[Code](https://github.com/laiguokun/LSTNet)]\n\n- [Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks](http://papers.nips.cc/paper/7977-forecasting-treatment-responses-over-time-using-recurrent-marginal-structural-networks.pdf) `NeurIPS 2018`\n\n  - Bryan Lim. et al.\n  - [Code](https://github.com/sjblim/rmsn_nips_2018)\n\n- [A Memory-Network Based Solution for Multivariate Time-Series Forecasting](https://arxiv.org/pdf/1809.02105.pdf)\n\n  - Yen-Yu Chang, et al.\n  - [Code-unofficial implementation](https://github.com/Maple728/MTNet)]\n\n### 2017\n\n- [Deep learning with long short-term memory networks for financial market predictions](https://www.econstor.eu/bitstream/10419/157808/1/886576210.pdf)\n\n  - Fischer, Thomas and Krauss, Christopher.\n  - Code not yet.\n\n- [Discriminative State-Space Models](https://papers.nips.cc/paper/7150-discriminative-state-space-models.pdf)`NIPS 2017`\n\n  - Vitaly Kuznetsov and Mehryar Mohri.\n  - Code not yet.\n\n- [Hybrid Neural Networks for Learning the Trend in Time Series](https://www.ijcai.org/Proceedings/2017/0316.pdf)**review**\n\n  - Tao Lin, et al.\n  - Code not yet.\n\n### 2016\n\n- [Data Preprocessing and Augmentation for Multiple Short Time Series Forecasting with Recurrent Neural Networks](https://www.researchgate.net/publication/309385800_Data_Preprocessing_and_Augmentation_for_Multiple_Short_Time_Series_Forecasting_with_Recurrent_Neural_Networks)\n\n   - Slawek Smyl and Karthik Kuber\n   - Code not yet.\n\n- [Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction](https://papers.nips.cc/paper/6160-temporal-regularized-matrix-factorization-for-high-dimensional-time-series-prediction)`NIPS 2016`\n\n  - Hsiang-Fu Yu, et al.\n  - [[Code](https://github.com/rofuyu/exp-trmf-nips16)]\n\n- [Time Series Prediction and Online Learning](http://proceedings.mlr.press/v49/kuznetsov16.pdf)`JMLR 2016`\n\n  - Vitaly Kuznetsov and Mehryar Mohri.\n  - Code not yet.\n\n- [Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S\u0026P 500](https://ideas.repec.org/p/zbw/iwqwdp/032016.html)\n\n  - Krauss, Christopher, et al.\n  - Code not yet.\n\n### Comparative: Classical methods vs Deep Learning methods\n\n- [Forecasting economic and financial time series: ARIMA VS. LSTM](https://arxiv.org/pdf/1803.06386.pdf)\n\n- [A comparative study between LSTM and ARIMA for sales forecasting in retail](https://pdfs.semanticscholar.org/e58c/7343ea25d05f6d859d66d6bb7fb91ecf9c2f.pdf)\n\n- [ARIMA/SARIMA vs LSTM with Ensemble learning Insights for Time Series Data](https://towardsdatascience.com/arima-sarima-vs-lstm-with-ensemble-learning-insights-for-time-series-data-509a5d87f20a)\n\n\n## Conferences\n\n- Machine learning\n   * [NeurIPS](https://nips.cc/)\n   * [ICML](https://icml.cc/)\n   * [ICLR](https://iclr.cc/)\n\n- Artificial intelligence\n   * [AAAI](https://www.aaai.org/)\n   * [AISTATS](https://www.aistats.org/)\n   * [ICANN](https://e-nns.org/icann2019/)\n   * [IJCAI](https://www.ijcai.org/)\n   * [UAI](http://www.auai.org/)\n\n## Competitions\n\n- [M5 Competition](https://mofc.unic.ac.cy/m5-competition/)\n- [M4 Competition](https://github.com/Mcompetitions/M4-methods)\n\n## Code\n\n- [Notebooks](https://github.com/Alro10/deep-learning-time-series/tree/master/notebooks)\n- [Code]()\n\n## Theory-Resource\n\n- [Time Series Forecasting Best Practices \u0026 Examples from Microsoft](https://github.com/microsoft/forecasting)\n\n- [Attention-for-time-series-classification-and-forecasting](https://towardsdatascience.com/attention-for-time-series-classification-and-forecasting-261723e0006d)\n\n- [Deep learning for high dimensional time series-blog](https://towardsdatascience.com/deep-learning-for-high-dimensional-time-series-7a72b033a7e0)\n\n- [Deep Learning AI-Optimization](https://deeplearning.ai/ai-notes/optimization/)\n\n- [Backpropagation for LSTM](https://towardsdatascience.com/back-to-basics-deriving-back-propagation-on-simple-rnn-lstm-feat-aidan-gomez-c7f286ba973d)\n\n- [Stock Market Prediction by Recurrent Neural Network on LSTM Model](https://blog.usejournal.com/stock-market-prediction-by-recurrent-neural-network-on-lstm-model-56de700bff68)\n\n- [Decoupling Hierarchical Recurrent Neural Networks With Locally Computable Losses](https://arxiv.org/pdf/1910.05245.pdf)\n\n- [Time Series Analysis with Deep Learning : Simplified](https://towardsdatascience.com/time-series-analysis-with-deep-learning-simplified-5c444315d773)\n\n- [ML techniques applied to stock prices](https://towardsdatascience.com/machine-learning-techniques-applied-to-stock-price-prediction-6c1994da8001)\n\n- [Forecasting: Principles and Practice: Slides](https://github.com/robjhyndman/ETC3550Slides)**Good material**\n\n## Code-Resource\n\n- [Transformer Time Series Prediction](https://github.com/oliverguhr/transformer-time-series-prediction)\n\n- [DeepSeries: Deep Learning Models for time series prediction.](https://github.com/EvilPsyCHo/Deep-Time-Series-Prediction)\n\n- [varstan: An R package for Bayesian analysis of structured time series models with Stan](https://arxiv.org/pdf/2005.10361.pdf)\n\n- [Time-series Generative Adversarial Networks: tsgan](https://github.com/firmai/tsgan)\n\n- [Deep4cast: Forecasting for Decision Making under Uncertainty](https://github.com/MSRDL/Deep4Cast)\n\n- [fireTS: sklean style package for multi-variate time-series prediction.](https://github.com/jxx123/fireTS)\n\n- [EpiSoon: Forecasting the effective reproduction number over short timescales](https://github.com/epiforecasts/EpiSoon)\n\n- [Electric Load Forecasting](https://github.com/pyaf/load_forecasting): Load forecasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models.\n\n- [Time Series and Forecasting in R](https://github.com/rstudio-conf-2020/time-series-forecasting)\n\n- [TimeseriesAI](https://github.com/timeseriesAI/timeseriesAI): Practical Deep Learning for Time Series / Sequential Data using fastai/ Pytorch.\n\n- [TimescaleDB](https://github.com/timescale/timescaledb): An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.\n\n- [TSstudio: Tools for time series analysis and forecasting ](https://github.com/RamiKrispin/TSstudio)\n\n- [Prophet: Automatic Forecasting Procedure](https://github.com/facebook/prophet)\n\n- [pyts: a Python package for time series classification](https://github.com/johannfaouzi/pyts)\n\n- [Using attentive neural processes for forecasting power usage](https://github.com/wassname/attentive-neural-processes)\n\n- [Non-Gaussian forecasting using fable - R](https://robjhyndman.com/hyndsight/fable2/)\n\n- [SKTIME](https://github.com/alan-turing-institute/sktime)\n\n- [Papers with code - Multivariate time series forecasting](https://paperswithcode.com/task/multivariate-time-series-forecasting)\n\n- [DeepAR by Amazon](https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html)\n\n- [DFGP by Amazon](https://aws.amazon.com/blogs/machine-learning/now-available-in-amazon-sagemaker-deepar-algorithm-for-more-accurate-time-series-forecasting/)\n\n- https://www.kaggle.com/c/demand-forecasting-kernels-only\n\n- https://www.kaggle.com/c/favorita-grocery-sales-forecasting\n\n- https://www.kaggle.com/c/grupo-bimbo-inventory-demand\n\n- https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting\n\n- [Predicting/hypothesizing the findings of the M4 Competition](https://www.sciencedirect.com/science/article/pii/S0169207019301098)\n\n- [pytorch-forecasting](https://github.com/jdb78/pytorch-forecasting): A Python package for time series forecasting with PyTorch. It includes state-of-the-art network architectures\n\n## Datasets\n\n- [A curated list of awesome time series databases](https://github.com/xephonhq/awesome-time-series-database)\n\n- [Electricity dataset from UCI](https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014)\n\n- [Traffic dataset from UCI](https://archive.ics.uci.edu/ml/datasets/PEMS-SF)\n\n- [Air quality from UCI](http://archive.ics.uci.edu/ml/datasets/Air+Quality)\n\n- [Seattle freeway traffic speed](https://github.com/zhiyongc/Seattle-Loop-Data)\n\n- [Kaggle-Web Traffic Time Series Forecasting](https://www.kaggle.com/c/web-traffic-time-series-forecasting)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falro10%2Fdeep-learning-time-series","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falro10%2Fdeep-learning-time-series","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falro10%2Fdeep-learning-time-series/lists"}