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

• 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 2020

  • 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

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

  • Christos Faloutsos, et al.
  • [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

  • 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

• 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]

• 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.

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

  • [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

• Notebooks
• Code

• 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

• 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

• 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