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...

• Papers
• Conferences
• Competitions
• Code
• Theory-Resource
• Code Resource
• Datasets

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

  • Haixu Wu, et al.
  • [Code]

• Long Range Probabilistic Forecasting in Time-Series using High Order Statistics

  • Prathamesh Deshpande, et al.
  • [Code]

• Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Networks

  • Maosen Li, et al.
  • Code not yet.

• End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series

  • Syama Sundar Rangapuram, et al.
  • Code not yet.

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

  • Kin G. Olivares, et al.
  • [Code]

• Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting reference

  • Kashif Rasul, et al.
  • [Code]

• An Experimental Review on Deep Learning Architectures for Time Series Forecasting

  • Pedro Lara-Benítez, et al.
  • [Code]

• Long Horizon Forecasting With Temporal Point Processes

  • Prathamesh Deshpande, et al.
  • [Code]

• Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting AAAI 2021

  • Haoyi Zhou, et al.
  • [Code]

• CHALLENGES AND APPROACHES TO TIME-SERIES FORECASTING IN DATA CENTER TELEMETRY: A SURVEY

  • 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

  • H.D. Nguyen, et al.
  • Code not yet.

• Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics

  • Ján Drgona, et al.
  • Code not yet.

• MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification

  • Angus Dempster, et al.
  • [Code]

• Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction

  • Yuan Xue, et al.
  • Code not yet.

• Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning

  • Castellani Andrea, et al.
  • Honda Research Institute Europe GmbH
  • Code not yet.

• Inter-Series Attention Model for COVID-19 Forecasting Good reference

  • Xiaoyong Jin, et al.
  • [Code]

• MODEL SELECTION IN RECONCILING HIERARCHICAL TIME SERIES

  • M. ABOLGHASEMI, et al.
  • [Code]

• A Strong Baseline for Weekly Time Series Forecasting

  • Rakshitha Godahewa, et al.
  • [Code]

• Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning

  • Trey McNeely, et al.
  • Code not yet.

• Modeling Heterogeneous Seasonality With Recurrent Neural Networks Using IoT Time Series Data for Defrost Detection and Anomaly Analysis Good Reference

  • Khetarpal, Suraj.
  • Code not yet.

• An Examination of the State-of-the-Art for Multivariate Time Series Classification

  • Bhaskar Dhariyal, et al.
  • Code noy yet.

• Rank Position Forecasting in Car Racing

  • Bo Peng, et al.
  • Code not yet.

• Mixed Membership Recurrent Neural Networks for Modeling Customer Purchases

  • Ghazal Fazelnia, et al.
  • Code not yet.

• An analysis of deep neural networks for predicting trends in time series data

  • Kouame Kouassi and Deshendran Moodley.
  • Code not yet.

• Automatic Forecasting using Gaussian Processes

  • G. Corani
  • Code not yet.

• Attention based Multi-Modal New Product Sales Time-series Forecasting

  • Vijay Ekambaram
  • Code not yet.

• Demand Forecasting of individual Probability Density Functions with Machine Learning

  • Felix Wick, et al.
  • Code not yet.

• A Time-Series Forecasting Performance Comparison for Neural Networks with State Space and ARIMA Models

  • Milton Soto-Ferrari
  • Code not yet.

• Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

  • Karthick Thiyagarajan, et al.
  • Code not yet.

• Multivariate Time-series Anomaly Detection via Graph Attention Network

  • Hang Zhao, et al.
  • Code not yet.

• Graph Neural Networks for Model Recommendation using Time Series Data

  • Aleksandr Pletnev, et al.
  • Code not yet.

• Kaggle forecasting competitions: An overlooked learning opportunity

  • Casper Solheim Bojer and Jens Peder Meldgaard.
  • [Code]

• Forecasting with Multiple Seasonality

  • Tianyang Xie and Jie Ding.
  • Code not yet.

• LAVARNET: Neural network modeling of causal variable relationships for multivariate time series forecasting

  • Christos Koutlis, et al.
  • Code not yet.

• Forecasting Hierarchical Time Series with a Regularized Embedding Space

  • Jeffrey L. Gleason.
  • [Code]

• Forecasting the Evolution of Hydropower Generation

  • Fan Zhou, et al.
  • [Code]

• Deep State-Space Generative Model For Correlated Time-to-Event Predictions

  • Yuan Xue, et al.
  • Code not yet.

• Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries

  • Fantazzini, Dean.
  • Code not yet.

• Scalable Low-Rank Autoregressive Tensor Learning for Spatiotemporal Traffic Data Imputation

  • Xinyu Chen, et al.
  • [Code]

• clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series

  • Daniel Jarrett, et al.
  • Code not yet.

• Speed Anomalies and Safe Departure Times from Uber Movement Data

  • Nabil Al Nahin Ch, et al.
  • Code not yet.

• Forecasting AI Progress: A Research Agenda

  • Ross Gruetzemacher, et al.
  • Review

• Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation

  • Kasun Bandara, et al.
  • Code not yet.

• Interpretable Sequence Learning for COVID-19 Forecasting

  • Sercan O. Arık, et al.
  • [Code]

• Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records meta-learning

  • Jiatu Shi, et al.
  • Code not yet.

• Forecasting Economic Recession through Share Price in the Logistics Industry with Artificial Intelligence (AI)

  • YM Tang, et al.
  • Code not yet.

• PRINCIPLES AND ALGORITHMS FOR FORECASTING GROUPS OF TIME SERIES: LOCALITY AND GLOBALITY

  • Pablo Montero-Manso and Rob J Hyndman
  • Code not yet.

• Multi-stream RNN for Merchant Transaction Prediction

  • Zhongfang Zhuang, et al.
  • KDD 2020 Workshop on Machine Learning in Finance
  • Code not yet.

• Prediction of hierarchical time series using structured regularization and its application to artificial neural networks

  • Tomokaze Shiratori, et al.
  • Code not yet.

• Cold-Start Promotional Sales Forecasting through Gradient Boosted-based Contrastive Explanations

  • Carlos Aguilar-Palacios, et al.
  • [Code]

• Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models

  • Fadhel Ayed, et al.
  • Amazon Research
  • [Code]

• Demand Forecasting in the Presence of Privileged Information

  • Mozhdeh Ariannezhad, et al.
  • [Code]

• Seasonal Self-evolving Neural Networks Based Short-term Wind Farm Generation Forecast

  • Yunchuan Liu, et al.
  • Code not yet.

• Distributed ARIMA Models for Ultra-long Time Series Spark

  • Xiaoqian Wang, et al.
  • [Code]

• Adversarial Attacks on Probabilistic Autoregressive Forecasting Models

  • Raphaël Dang-Nhu, et al.
  • [Code]

• Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction LSTM application

  • Alexander Acker, et al.
  • [Code]

• Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

  • Lei Bai, et al.
  • [Code]

• Dynamic Multi-Scale Convolutional Neural Network for Time Series Classification

  • BIN QIAN, et al.
  • Code not yet.

• Neural Architecture Search for Time Series Classification

  • Hojjat Rakhshani, et al.
  • [Code]

• Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions

  • Ahmed M. Alaa and Mihaela van der Schaar.
  • Code not yet.

• Time Series Regression

  • Chang Wei Tan, et al.
  • [Code]

• Forecasting Supplier Delivery Performance with Recurrent Neural Networks

  • Johan Ramne
  • Master Thesis.

• Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments

  • Fatih Ilhan, et al.
  • Code not yet.

• Resilient Neural Forecasting Systems

  • Michael Bohlke-Schneider, et al.
  • Amazon Research
  • Code not yet.

• Dynamic Neural Relational Inference for Forecasting Trajectories

  • Colin Graber and Alexander Schwing
  • CVPR 2020
  • [Code]

• Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting

  • Ling Cai, et al.
  • Code not yet.

• Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series

  • Anna K. Yanchenko and Sayan Mukherjee.
  • Code not yet.

• Neuroevolution Strategy for Time Series Prediction

  • George Naskos, et al.
  • Code not yet.

• COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population

  • Vasilis Papastefanopoulos, et al.
  • [Code]

• A machine learning approach for forecasting hierarchical time series

  • Paolo Mancuso, et al.
  • Code not yet.

• ProbCast: Open-source Production, Evaluation and Visualisation of Probabilistic Forecasts

  • Jethro Browell and Ciaran Gilbert.
  • [Code]

• Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Modelsmeta-learning

  • Sibghat Ullah, et al.
  • [Code]

• Semisupervised Deep State-Space Model for Plant Growth Modeling

  • S. Shibata, et al.
  • Code not yet.

• EFFECTIVE AND EFFICIENT COMPUTATION WITH MULTIPLE-TIMESCALE SPIKING RECURRENT NEURAL NETWORKS

  • Bojian Yin, et al.
  • Code not yet.

• Multivariate time series forecasting via attention-based encoder–decoder framework

  • Shengdong Du, et al.
  • Neurocomputing
  • Code not yet.

• A Novel LSTM for Multivariate Time Series with Massive Missingness

  • Nazanin Fouladgar and Kary Främling.
  • Code not yet.

• N-BEATS: NEURAL BASIS EXPANSION ANALYSIS FOR INTERPRETABLE TIME SERIES FORECASTING ICLR 2020

  • Boris N. Oreshkin, et al.
  • Code not yet.

• How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecastinggood new approach

  • Robin Hirt, et al.
  • Code not yet.

• The Hybrid Forecasting Method SVR-ESAR for Covid-19

  • Juan Frausto Solis, et al.
  • Code not yet.

• Forecasting the Short-Term Metro Ridership With Seasonal and Trend Decomposition Using Loess and LSTM Neural Networks

  • DEWANG CHEN, et al.
  • Code not yet.

• The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models

  • Stephan Rabanser, et al.
  • AWS AI Labs
  • Code not yet.

• FORECASTING WITH SKTIME: DESIGNING SKTIME’S NEW FORECASTING API AND APPLYING IT TO REPLICATE AND EXTEND THE M4 STUDY

  • Markus Löning and Franz J. Király.
  • [Code]

• LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns

  • Kasun Bandara, et al.
  • [Code]

• A NETWORK-BASED TRANSFER LEARNING APPROACH TO IMPROVE SALES FORECASTING OF NEW PRODUCTS

  • Karb, Tristan, et al.
  • Code not yet.

• DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting Good new approach

  • Siteng Huang, et al.
  • [Code]

• An Approach for Complex Event Streams Processing and Forecasting

  • Viktor Morozov, Mikhail Petrovskiy.
  • Code not yet.

• Knowledge Enhanced Neural Fashion Trend Forecasting

  • Yunshan Ma, et al.
  • Code not yet.

• Augmented Out-of-Sample Comparison Method for Time Series Forecasting Techniques

  • Igor Ilic, et al.
  • Code not yet.

• Enhancing High Frequency Technical Indicators Forecasting Using Shrinking Deep Neural Networks ICIM 2020

  • Xiaoyu Tan, et al.
  • Code not yet.

• Time Series Forecasting With Deep Learning: A Survey Good summary

  • Bryan Lim and Stefan Zohren
  • Survey

• Neural forecasting: Introduction and literature overview

  • Konstantinos Benidis, et al.
  • Not is a overview.

• Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories

  • Hao Xue, et al.
  • Code not yet.

• Orbit: Probabilistic Forecast with Exponential Smoothing

  • Edwin Ng, et a.
  • Code is available upon request.

• Daily retail demand forecasting using machine learning with emphasis on calendric special days

  • Jakob Huber and Heiner Stuckenschmidt.
  • Code not yet.

• FORECASTING IN MULTIVARIATE IRREGULARLY SAMPLED TIME SERIES WITH MISSING VALUES

  • Shivam Srivastava, et al.
  • Code not yet.
  • IBM Almaden Research Center.

• Multi-label Prediction in Time Series Data using Deep Neural Networks

  • Wenyu Zhang, et al.
  • Code not yet.

• TraDE: Transformers for Density Estimation

  • Rasool Fakoor, et al.
  • Code not yet.

• Deep Probabilistic Modelling of Price Movements for High-Frequency Trading

  • Ye-Sheen Lim and Denise Gorse.
  • Code not yet.

• Deep State Space Models for Nonlinear System Identification

  • Daniel Gedon, et al.
  • Code not yet.

• Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks

  • Bernardo Perez Orozco and Stephen J. Roberts.
  • [Code]

• Financial Time Series Representation Learning

  • Philippe Chatigny, et al.
  • Code not yet.

• G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes

  • Rui Li, et al.
  • • IBM research and MIT
  • Code not yet.

• Deep Markov Spatio-Temporal Factorization

  • Amirreza Farnoosh, et al.
  • Code not yet.

• Harmonic Recurrent Process for Time Series Forecasting

  • Shao-Qun Zhang and Zhi-Hua Zhou.
  • Code not yet.

• Elastic Machine Learning Algorithms in Amazon SageMaker

  • Edo Liberty, et al.
  • Code not yet.

• Time Series Data Augmentation for Deep Learning: A Survey

  • Qingsong Wen, et al.
  • Code not yet.

• Block Hankel Tensor ARIMA for Multiple Short Time Series ForecastingAAAI 2020meta-learning

  • QIQUAN SHI, et al.
  • [Code]

• Learnings from Kaggle's Forecasting Competitions

  • Casper Solheim Bojer, et al.
  • Code not yet.

• An Industry Case of Large-Scale Demand Forecasting of Hierarchical Components

  • Rodrigo Rivera-Castro, et al.
  • Code not yet.

• Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows

  • Kashif Rasul, et al.
  • [Code].

• ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting

  • Joel Janek Dabrowski, et al.
  • Code not yet.

• Anomaly detection for Cybersecurity: time series forecasting and deep learningGood review about forecasting

  • Giordano Colò.
  • Code not yet.

• Event-Driven Continuous Time Bayesian Networks

  • Debarun Bhattacharjya, et al.
  • Research AI, IBM
  • Code not yet.

• Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing ValuesAAAI 2020

  • Xianfeng Tang, et al.
  • IBM Research, NY
  • Code not yet.

• Topology-Based Clusterwise Regression for User Segmentation and Demand Forecasting

  • Rodrigo Rivera-Castro, et al.
  • Code not yet.

• Evolutionary LSTM-FCN networks for pattern classification in industrial processes

  • Patxi Ortego, et al.
  • Code not yet.

• Forecasting Multivariate Time-Series Data Using LSTM and Mini-Batches

  • Athar Khodabakhsh, et al.
  • Code not yet.

• Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time SeriesAAAI 2020

  • Dongkuan Xu, et al.
  • [Code]

• RELATIONAL STATE-SPACE MODEL FOR STOCHASTIC MULTI-OBJECT SYSTEMSICLR 2020

  • Fan Yang, et al.
  • Code not yet.

• For2For: Learning to forecast from forecasts

  • Zhao, Shi, et al.
  • Code not yet.

• Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning AAAI 2020

  • Long H. Nguyen, et al.
  • Code not yet

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

  • Shiyang Li, et al.
  • [Code]

• Forecasting Big Time Series: Theory and PracticeKDD 2019 Relevant tutorial

  • Christos Faloutsos, et al.
  • [Code]

• Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting

  • Bin Wang, et al.
  • [Code]

• A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting

  • Slawek Smyl
  • Winning submission of the M4 forecasting competition
  • [Code]

• Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series ForecastingNeurIPS 2019

  • Rajat Sen, et al.
  • Amazon
  • [Code]

• Deep Landscape Forecasting for Real-time Bidding Advertising KDD 2019

  • Kan Ren, et al.
  • [Code]

• Similarity Preserving Representation Learning for Time Series Clustering

  • Qi Lei, et al.
  • IBM research
  • [Code]

• DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting

  • Siteng Huang, et al.
  • Code not yet.

• Enhancing Time Series Momentum Strategies Using Deep Neural Networks

  • Bryan Lim, et al.
  • Code not yet.

• DYNAMIC TIME LAG REGRESSION: PREDICTING WHAT & WHEN

  • Mandar Chandorkar, et al.
  • Code not yet.

• Time-series Generative Adversarial NetworksNeurIPS 2019

  • Jinsung Yoon. et al.
  • Code not yet.

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

  • Bryan Lim, et al.
  • Google Research
  • [Code]

• Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models

  • Vincent Fortuin, et al.
  • Code not yet.

• Deep Physiological State Space Model for Clinical Forecasting

  • Yuan Xue, et al.
  • not yet

• AR-Net: A simple Auto-Regressive Neural Network for time-series

  • Oskar Triebe, et al.
  • Facebook Research
  • Code not yet.

• Learning Time-series Data of Industrial Design Optimization using Recurrent Neural Networks

  • Sneha Saha, et al.
  • Honda Research Institute Europe GmbH
  • Code not yet.

• RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

  • Qingsong Wen, et al.
  • [Code]

• Constructing Gradient Controllable Recurrent Neural Networks Using Hamiltonian Dynamics

  • Konstantin Rusch, et al.
  • Code not yet.

• SOM-VAE: Interpretable Discrete Representation Learning on Time SeriesICLR 2019

  • Vincent Fortuin, et al.
  • [Code]

• Unsupervised Scalable Representation Learning for Multivariate Time SeriesNeurIPS 2019 In Applications -- Time Series Analysis

  • Jean-Yves Franceschi, et al.
  • [Code]

• Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series

  • Zhi-Xuan Tan, et al.
  • Code not yet.

• You May Not Need Order in Time Series Forecasting

  • Yunkai Zhang, et al.
  • Code not yet

  

• Shape and Time Distortion Loss for Training Deep Time Series Forecasting ModelsNeurIPS2019

  • Vincent Le Guen and Nicolas Thome.
  • [Code]

• Dynamic Local Regret for Non-convex Online ForecastingNeurIPS 2019

  • Sergul Aydore, et al.
  • [Code]

• Bayesian Temporal Factorization for Multidimensional Time Series Prediction

  • Xinyu Chen, and Lijun Sun
  • [Code and data]

• Probabilistic sequential matrix factorization

  • Ömer Deniz Akyildiz, et al.
  • Code not yet

• Sequential VAE-LSTM for Anomaly Detection on Time Series

  • Run-Qing Chen, et al.
  • Code not yet

• High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula ProcessesNeurIPS 2019

  • David Salinas, et al.
  • Code not yet

• Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction

  • Bryan Lim, et al.
  • Code not yet

• LHCnn: A Novel Efficient Multivariate Time Series Prediction Framework Utilizing Convolutional Neural Networks

  • Chengxi Liu, et al.
  • Code not yet

• SKTIME: A UNIFIED INTERFACE FOR MACHINE LEARNING WITH TIME SERIE

  • [Code]

• Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions

  • [Code]

• 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

  • Antonio Rafael Sabino Parmezan, Vinicius M. A. Souza and Gustavo E. A. P. A. Batista. USP

• Explainable Deep Neural Networks for Multivariate Time Series Predictions IJCAI 2019

  • Roy Assaf and Anika Schumann.
  • IBM Research, Zurich
  • Code not yet

• Outlier Detection for Time Series with Recurrent Autoencoder Ensembles IJCAI 2019

  • [Code]

• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting IJCAI 2019

  • Code not yet

• Deep Factors for Forecasting ICML 2019

  • Yuyang Wang, et al.
  • Code not yet

• Probabilistic Forecasting with Spline Quantile Function RNNs

  • Code not yet

• Deep learning for time series classification: a review

  • Code not yet

• Multivariate LSTM-FCNs for Time Series Classification

  • Code not yet

• Criteria for classifying forecasting methods

  • Code not yet

• GluonTS: Probabilistic Time Series Models in Python

  • [Code]

• DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

  • David Salinas, et al.
  • Code not yet

• An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting

  • Filippo Maria Bianchi, et al.
  • Code not yet.

• Statistical and Machine Learning forecasting methods: Concerns and ways forward

  • Spyros Makridakis, et al.
  • Code not yet.

• Attend and Diagnose: Clinical Time Series Analysis Using Attention Models AAAI 2018

  • Huan Song, Deepta Rajan, et al.
  • not yet.

• Precision and Recall for Time Series NeurIPS2018

  • Nesime Tatbul, et al.
  • Code not yet.

• Deep State Space Models for Time Series Forecasting NeurIPS2018

  • Code not yet

• Deep Factors with Gaussian Processes for Forecasting

  • Third workshop on Bayesian Deep Learning (NeurIPS 2018)
  • [Code]

• DIFFUSION CONVOLUTIONAL RECURRENT NEURAL NETWORK: DATA-DRIVEN TRAFFIC FORECASTINGICLR 2018

  • Yaguang Li, et al.
  • [Code]

• DEEP TEMPORAL CLUSTERING: FULLY UNSUPERVISED LEARNING OF TIME-DOMAIN FEATURES

  • Naveen Sai Madiraju, et al.
  • [Code-unofficial implementation ]

• Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

  • Guokun Lai, Wei-Cheng Chang, Yiming Yang, Hanxiao Liu
  • [Code]

• Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks NeurIPS 2018

  • Bryan Lim. et al.
  • Code

• A Memory-Network Based Solution for Multivariate Time-Series Forecasting

  • Yen-Yu Chang, et al.
  • Code-unofficial implementation]

• Deep learning with long short-term memory networks for financial market predictions

  • Fischer, Thomas and Krauss, Christopher.
  • Code not yet.

• Discriminative State-Space ModelsNIPS 2017

  • Vitaly Kuznetsov and Mehryar Mohri.
  • Code not yet.

• Hybrid Neural Networks for Learning the Trend in Time Seriesreview

  • Tao Lin, et al.
  • Code not yet.

• Data Preprocessing and Augmentation for Multiple Short Time Series Forecasting with Recurrent Neural Networks

  • Slawek Smyl and Karthik Kuber
  • Code not yet.

• Temporal Regularized Matrix Factorization for High-dimensional Time Series PredictionNIPS 2016

  • Hsiang-Fu Yu, et al.
  • [Code]

• Time Series Prediction and Online LearningJMLR 2016

  • Vitaly Kuznetsov and Mehryar Mohri.
  • Code not yet.

• Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500

  • Krauss, Christopher, et al.
  • Code not yet.

• Forecasting economic and financial time series: ARIMA VS. LSTM

• A comparative study between LSTM and ARIMA for sales forecasting in retail

• ARIMA/SARIMA vs LSTM with Ensemble learning Insights for Time Series Data

• Machine learning

  • NeurIPS
  • ICML
  • ICLR

• Artificial intelligence

  • AAAI
  • AISTATS
  • ICANN
  • IJCAI
  • UAI

• M5 Competition
• M4 Competition

• Notebooks
• Code

• Time Series Forecasting Best Practices & Examples from Microsoft

• Attention-for-time-series-classification-and-forecasting

• Deep learning for high dimensional time series-blog

• Deep Learning AI-Optimization

• Backpropagation for LSTM

• Stock Market Prediction by Recurrent Neural Network on LSTM Model

• Decoupling Hierarchical Recurrent Neural Networks With Locally Computable Losses

• Time Series Analysis with Deep Learning : Simplified

• ML techniques applied to stock prices

• Forecasting: Principles and Practice: SlidesGood material

• Transformer Time Series Prediction

• DeepSeries: Deep Learning Models for time series prediction.

• varstan: An R package for Bayesian analysis of structured time series models with Stan

• Time-series Generative Adversarial Networks: tsgan

• Deep4cast: Forecasting for Decision Making under Uncertainty

• fireTS: sklean style package for multi-variate time-series prediction.

• EpiSoon: Forecasting the effective reproduction number over short timescales

• Electric Load Forecasting: Load forecasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models.

• Time Series and Forecasting in R

• TimeseriesAI: Practical Deep Learning for Time Series / Sequential Data using fastai/ Pytorch.

• TimescaleDB: An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.

• TSstudio: Tools for time series analysis and forecasting

• Prophet: Automatic Forecasting Procedure

• pyts: a Python package for time series classification

• Using attentive neural processes for forecasting power usage

• Non-Gaussian forecasting using fable - R

• SKTIME

• Papers with code - Multivariate time series forecasting

• DeepAR by Amazon

• DFGP by Amazon

• https://www.kaggle.com/c/demand-forecasting-kernels-only

• https://www.kaggle.com/c/favorita-grocery-sales-forecasting

• https://www.kaggle.com/c/grupo-bimbo-inventory-demand

• https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting

• Predicting/hypothesizing the findings of the M4 Competition

• pytorch-forecasting: A Python package for time series forecasting with PyTorch. It includes state-of-the-art network architectures

• A curated list of awesome time series databases

• Electricity dataset from UCI

• Traffic dataset from UCI

• Air quality from UCI

• Seattle freeway traffic speed

• Kaggle-Web Traffic Time Series Forecasting