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...
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Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Networks
- Maosen Li, et al.
- Code not yet.
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End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series
- Syama Sundar Rangapuram, et al.
- Code not yet.
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Neural basis expansion analysis with exogenous variables:Forecasting electricity prices with NBEATSx
- Kin G. Olivares, et al.
- [Code]
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Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting reference
- Kashif Rasul, et al.
- [Code]
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An Experimental Review on Deep Learning Architectures for Time Series Forecasting
- Pedro Lara-Benítez, et al.
- [Code]
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Long Horizon Forecasting With Temporal Point Processes
- Prathamesh Deshpande, et al.
- [Code]
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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
AAAI 2021
- Haoyi Zhou, et al.
- [Code]
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CHALLENGES AND APPROACHES TO TIME-SERIES FORECASTING IN DATA CENTER TELEMETRY: A SURVEY
- Shruti Jadon, et al.
- Code not yet.
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- H.D. Nguyen, et al.
- Code not yet.
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Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics
- Ján Drgona, et al.
- Code not yet.
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MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification
- Angus Dempster, et al.
- [Code]
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Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction
- Yuan Xue, et al.
- Code not yet.
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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.
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Inter-Series Attention Model for COVID-19 Forecasting Good reference
- Xiaoyong Jin, et al.
- [Code]
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MODEL SELECTION IN RECONCILING HIERARCHICAL TIME SERIES
- M. ABOLGHASEMI, et al.
- [Code]
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A Strong Baseline for Weekly Time Series Forecasting
- Rakshitha Godahewa, et al.
- [Code]
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- Trey McNeely, et al.
- Code not yet.
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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.
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An Examination of the State-of-the-Art for Multivariate Time Series Classification
- Bhaskar Dhariyal, et al.
- Code noy yet.
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Rank Position Forecasting in Car Racing
- Bo Peng, et al.
- Code not yet.
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Mixed Membership Recurrent Neural Networks for Modeling Customer Purchases
- Ghazal Fazelnia, et al.
- Code not yet.
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An analysis of deep neural networks for predicting trends in time series data
- Kouame Kouassi and Deshendran Moodley.
- Code not yet.
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Automatic Forecasting using Gaussian Processes
- G. Corani
- Code not yet.
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Attention based Multi-Modal New Product Sales Time-series Forecasting
- Vijay Ekambaram
- Code not yet.
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Demand Forecasting of individual Probability Density Functions with Machine Learning
- Felix Wick, et al.
- Code not yet.
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- Milton Soto-Ferrari
- Code not yet.
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Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature
- Karthick Thiyagarajan, et al.
- Code not yet.
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Multivariate Time-series Anomaly Detection via Graph Attention Network
- Hang Zhao, et al.
- Code not yet.
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Graph Neural Networks for Model Recommendation using Time Series Data
- Aleksandr Pletnev, et al.
- Code not yet.
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Kaggle forecasting competitions: An overlooked learning opportunity
- Casper Solheim Bojer and Jens Peder Meldgaard.
- [Code]
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Forecasting with Multiple Seasonality
- Tianyang Xie and Jie Ding.
- Code not yet.
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- Christos Koutlis, et al.
- Code not yet.
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Forecasting Hierarchical Time Series with a Regularized Embedding Space
- Jeffrey L. Gleason.
- [Code]
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Forecasting the Evolution of Hydropower Generation
- Fan Zhou, et al.
- [Code]
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Deep State-Space Generative Model For Correlated Time-to-Event Predictions
- Yuan Xue, et al.
- Code not yet.
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- Fantazzini, Dean.
- Code not yet.
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Scalable Low-Rank Autoregressive Tensor Learning for Spatiotemporal Traffic Data Imputation
- Xinyu Chen, et al.
- [Code]
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clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series
- Daniel Jarrett, et al.
- Code not yet.
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Speed Anomalies and Safe Departure Times from Uber Movement Data
- Nabil Al Nahin Ch, et al.
- Code not yet.
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Forecasting AI Progress: A Research Agenda
- Ross Gruetzemacher, et al.
- Review
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Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation
- Kasun Bandara, et al.
- Code not yet.
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Interpretable Sequence Learning for COVID-19 Forecasting
- Sercan O. Arık, et al.
- [Code]
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Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records meta-learning
- Jiatu Shi, et al.
- Code not yet.
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- YM Tang, et al.
- Code not yet.
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PRINCIPLES AND ALGORITHMS FOR FORECASTING GROUPS OF TIME SERIES: LOCALITY AND GLOBALITY
- Pablo Montero-Manso and Rob J Hyndman
- Code not yet.
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Multi-stream RNN for Merchant Transaction Prediction
- Zhongfang Zhuang, et al.
KDD 2020 Workshop on Machine Learning in Finance
- Code not yet.
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- Tomokaze Shiratori, et al.
- Code not yet.
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Cold-Start Promotional Sales Forecasting through Gradient Boosted-based Contrastive Explanations
- Carlos Aguilar-Palacios, et al.
- [Code]
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Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
- Fadhel Ayed, et al.
Amazon Research
- [Code]
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Demand Forecasting in the Presence of Privileged Information
- Mozhdeh Ariannezhad, et al.
- [Code]
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Seasonal Self-evolving Neural Networks Based Short-term Wind Farm Generation Forecast
- Yunchuan Liu, et al.
- Code not yet.
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Distributed ARIMA Models for Ultra-long Time Series Spark
- Xiaoqian Wang, et al.
- [Code]
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Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
- Raphaël Dang-Nhu, et al.
- [Code]
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Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction LSTM application
- Alexander Acker, et al.
- [Code]
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Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
- Lei Bai, et al.
- [Code]
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Dynamic Multi-Scale Convolutional Neural Network for Time Series Classification
- BIN QIAN, et al.
- Code not yet.
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Neural Architecture Search for Time Series Classification
- Hojjat Rakhshani, et al.
- [Code]
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Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
- Ahmed M. Alaa and Mihaela van der Schaar.
- Code not yet.
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- Chang Wei Tan, et al.
- [Code]
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Forecasting Supplier Delivery Performance with Recurrent Neural Networks
- Johan Ramne
- Master Thesis.
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- Fatih Ilhan, et al.
- Code not yet.
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Resilient Neural Forecasting Systems
- Michael Bohlke-Schneider, et al.
Amazon Research
- Code not yet.
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Dynamic Neural Relational Inference for Forecasting Trajectories
- Colin Graber and Alexander Schwing
CVPR 2020
- [Code]
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Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting
- Ling Cai, et al.
- Code not yet.
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Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series
- Anna K. Yanchenko and Sayan Mukherjee.
- Code not yet.
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Neuroevolution Strategy for Time Series Prediction
- George Naskos, et al.
- Code not yet.
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COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
- Vasilis Papastefanopoulos, et al.
- [Code]
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A machine learning approach for forecasting hierarchical time series
- Paolo Mancuso, et al.
- Code not yet.
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ProbCast: Open-source Production, Evaluation and Visualisation of Probabilistic Forecasts
- Jethro Browell and Ciaran Gilbert.
- [Code]
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Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Modelsmeta-learning
- Sibghat Ullah, et al.
- [Code]
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Semisupervised Deep State-Space Model for Plant Growth Modeling
- S. Shibata, et al.
- Code not yet.
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EFFECTIVE AND EFFICIENT COMPUTATION WITH MULTIPLE-TIMESCALE SPIKING RECURRENT NEURAL NETWORKS
- Bojian Yin, et al.
- Code not yet.
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Multivariate time series forecasting via attention-based encoder–decoder framework
- Shengdong Du, et al.
Neurocomputing
- Code not yet.
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A Novel LSTM for Multivariate Time Series with Massive Missingness
- Nazanin Fouladgar and Kary Främling.
- Code not yet.
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N-BEATS: NEURAL BASIS EXPANSION ANALYSIS FOR INTERPRETABLE TIME SERIES FORECASTING
ICLR 2020
- Boris N. Oreshkin, et al.
- Code not yet.
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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.
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The Hybrid Forecasting Method SVR-ESAR for Covid-19
- Juan Frausto Solis, et al.
- Code not yet.
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- DEWANG CHEN, et al.
- Code not yet.
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The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models
- Stephan Rabanser, et al.
AWS AI Labs
- Code not yet.
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- Markus Löning and Franz J. Király.
- [Code]
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LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns
- Kasun Bandara, et al.
- [Code]
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A NETWORK-BASED TRANSFER LEARNING APPROACH TO IMPROVE SALES FORECASTING OF NEW PRODUCTS
- Karb, Tristan, et al.
- Code not yet.
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DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting Good new approach
- Siteng Huang, et al.
- [Code]
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An Approach for Complex Event Streams Processing and Forecasting
- Viktor Morozov, Mikhail Petrovskiy.
- Code not yet.
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Knowledge Enhanced Neural Fashion Trend Forecasting
- Yunshan Ma, et al.
- Code not yet.
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Augmented Out-of-Sample Comparison Method for Time Series Forecasting Techniques
- Igor Ilic, et al.
- Code not yet.
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Enhancing High Frequency Technical Indicators Forecasting Using Shrinking Deep Neural Networks
ICIM 2020
- Xiaoyu Tan, et al.
- Code not yet.
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Time Series Forecasting With Deep Learning: A Survey Good summary
- Bryan Lim and Stefan Zohren
- Survey
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Neural forecasting: Introduction and literature overview
- Konstantinos Benidis, et al.
- Not is a overview.
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Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories
- Hao Xue, et al.
- Code not yet.
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Orbit: Probabilistic Forecast with Exponential Smoothing
- Edwin Ng, et a.
- Code is available upon request.
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Daily retail demand forecasting using machine learning with emphasis on calendric special days
- Jakob Huber and Heiner Stuckenschmidt.
- Code not yet.
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FORECASTING IN MULTIVARIATE IRREGULARLY SAMPLED TIME SERIES WITH MISSING VALUES
- Shivam Srivastava, et al.
- Code not yet.
- IBM Almaden Research Center.
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Multi-label Prediction in Time Series Data using Deep Neural Networks
- Wenyu Zhang, et al.
- Code not yet.
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TraDE: Transformers for Density Estimation
- Rasool Fakoor, et al.
- Code not yet.
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Deep Probabilistic Modelling of Price Movements for High-Frequency Trading
- Ye-Sheen Lim and Denise Gorse.
- Code not yet.
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Deep State Space Models for Nonlinear System Identification
- Daniel Gedon, et al.
- Code not yet.
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Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks
- Bernardo Perez Orozco and Stephen J. Roberts.
- [Code]
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Financial Time Series Representation Learning
- Philippe Chatigny, et al.
- Code not yet.
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- Rui Li, et al.
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IBM research and MIT
- Code not yet.
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Deep Markov Spatio-Temporal Factorization
- Amirreza Farnoosh, et al.
- Code not yet.
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Harmonic Recurrent Process for Time Series Forecasting
- Shao-Qun Zhang and Zhi-Hua Zhou.
- Code not yet.
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Elastic Machine Learning Algorithms in Amazon SageMaker
- Edo Liberty, et al.
- Code not yet.
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Time Series Data Augmentation for Deep Learning: A Survey
- Qingsong Wen, et al.
- Code not yet.
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Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting
AAAI 2020
meta-learning- QIQUAN SHI, et al.
- [Code]
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Learnings from Kaggle's Forecasting Competitions
- Casper Solheim Bojer, et al.
- Code not yet.
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An Industry Case of Large-Scale Demand Forecasting of Hierarchical Components
- Rodrigo Rivera-Castro, et al.
- Code not yet.
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Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
- Kashif Rasul, et al.
- [Code].
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- Joel Janek Dabrowski, et al.
- Code not yet.
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Anomaly detection for Cybersecurity: time series forecasting and deep learning
Good review about forecasting
- Giordano Colò.
- Code not yet.
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Event-Driven Continuous Time Bayesian Networks
- Debarun Bhattacharjya, et al.
Research AI, IBM
- Code not yet.
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- Xianfeng Tang, et al.
IBM Research, NY
- Code not yet.
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Topology-Based Clusterwise Regression for User Segmentation and Demand Forecasting
- Rodrigo Rivera-Castro, et al.
- Code not yet.
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Evolutionary LSTM-FCN networks for pattern classification in industrial processes
- Patxi Ortego, et al.
- Code not yet.
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Forecasting Multivariate Time-Series Data Using LSTM and Mini-Batches
- Athar Khodabakhsh, et al.
- Code not yet.
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Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series
AAAI 2020
- Dongkuan Xu, et al.
- [Code]
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RELATIONAL STATE-SPACE MODEL FOR STOCHASTIC MULTI-OBJECT SYSTEMS
ICLR 2020
- Fan Yang, et al.
- Code not yet.
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For2For: Learning to forecast from forecasts
- Zhao, Shi, et al.
- Code not yet.
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Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning
AAAI 2020
- Long H. Nguyen, et al.
- Code not yet
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Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting Reference
- Shiyang Li, et al.
- [Code]
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Forecasting Big Time Series: Theory and Practice
KDD 2019
Relevant tutorial- Christos Faloutsos, et al.
- [Code]
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Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting
- Bin Wang, et al.
- [Code]
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A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
- Slawek Smyl
Winning submission of the M4 forecasting competition
- [Code]
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Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
NeurIPS 2019
- Rajat Sen, et al.
Amazon
- [Code]
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Deep Landscape Forecasting for Real-time Bidding Advertising
KDD 2019
- Kan Ren, et al.
- [Code]
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Similarity Preserving Representation Learning for Time Series Clustering
- Qi Lei, et al.
IBM research
- [Code]
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DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting
- Siteng Huang, et al.
- Code not yet.
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Enhancing Time Series Momentum Strategies Using Deep Neural Networks
- Bryan Lim, et al.
- Code not yet.
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DYNAMIC TIME LAG REGRESSION: PREDICTING WHAT & WHEN
- Mandar Chandorkar, et al.
- Code not yet.
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Time-series Generative Adversarial Networks
NeurIPS 2019
- Jinsung Yoon. et al.
- Code not yet.
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Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
- Bryan Lim, et al.
Google Research
- [Code]
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- Vincent Fortuin, et al.
- Code not yet.
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Deep Physiological State Space Model for Clinical Forecasting
- Yuan Xue, et al.
- not yet
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AR-Net: A simple Auto-Regressive Neural Network for time-series
- Oskar Triebe, et al.
Facebook Research
- Code not yet.
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Learning Time-series Data of Industrial Design Optimization using Recurrent Neural Networks
- Sneha Saha, et al.
Honda Research Institute Europe GmbH
- Code not yet.
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RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
- Qingsong Wen, et al.
- [Code]
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Constructing Gradient Controllable Recurrent Neural Networks Using Hamiltonian Dynamics
- Konstantin Rusch, et al.
- Code not yet.
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SOM-VAE: Interpretable Discrete Representation Learning on Time Series
ICLR 2019
- Vincent Fortuin, et al.
- [Code]
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Unsupervised Scalable Representation Learning for Multivariate Time Series
NeurIPS 2019
In Applications -- Time Series Analysis- Jean-Yves Franceschi, et al.
- [Code]
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Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series
- Zhi-Xuan Tan, et al.
- Code not yet.
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You May Not Need Order in Time Series Forecasting
- Yunkai Zhang, et al.
- Code not yet
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Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
NeurIPS2019
- Vincent Le Guen and Nicolas Thome.
- [Code]
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Dynamic Local Regret for Non-convex Online Forecasting
NeurIPS 2019
- Sergul Aydore, et al.
- [Code]
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Bayesian Temporal Factorization for Multidimensional Time Series Prediction
- Xinyu Chen, and Lijun Sun
- [Code and data]
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Probabilistic sequential matrix factorization
- Ömer Deniz Akyildiz, et al.
- Code not yet
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Sequential VAE-LSTM for Anomaly Detection on Time Series
- Run-Qing Chen, et al.
- Code not yet
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High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes
NeurIPS 2019
- David Salinas, et al.
- Code not yet
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Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
- Bryan Lim, et al.
- Code not yet
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- Chengxi Liu, et al.
- Code not yet
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SKTIME: A UNIFIED INTERFACE FOR MACHINE LEARNING WITH TIME SERIE
- [Code]
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Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
- [Code]
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- Antonio Rafael Sabino Parmezan, Vinicius M. A. Souza and Gustavo E. A. P. A. Batista. USP
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Explainable Deep Neural Networks for Multivariate Time Series Predictions
IJCAI 2019
- Roy Assaf and Anika Schumann.
IBM Research, Zurich
- Code not yet
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Outlier Detection for Time Series with Recurrent Autoencoder Ensembles
IJCAI 2019
- [Code]
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Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting
IJCAI 2019
- Code not yet
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Deep Factors for Forecasting
ICML 2019
- Yuyang Wang, et al.
- Code not yet
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Probabilistic Forecasting with Spline Quantile Function RNNs
- Code not yet
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Deep learning for time series classification: a review
- Code not yet
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Multivariate LSTM-FCNs for Time Series Classification
- Code not yet
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Criteria for classifying forecasting methods
- Code not yet
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GluonTS: Probabilistic Time Series Models in Python
- [Code]
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DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
- David Salinas, et al.
- Code not yet
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An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
- Filippo Maria Bianchi, et al.
- Code not yet.
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Statistical and Machine Learning forecasting methods: Concerns and ways forward
- Spyros Makridakis, et al.
- Code not yet.
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Attend and Diagnose: Clinical Time Series Analysis Using Attention Models
AAAI 2018
- Huan Song, Deepta Rajan, et al.
- not yet.
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Precision and Recall for Time Series
NeurIPS2018
- Nesime Tatbul, et al.
- Code not yet.
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Deep State Space Models for Time Series Forecasting
NeurIPS2018
- Code not yet
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Deep Factors with Gaussian Processes for Forecasting
Third workshop on Bayesian Deep Learning (NeurIPS 2018)
- [Code]
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DIFFUSION CONVOLUTIONAL RECURRENT NEURAL NETWORK: DATA-DRIVEN TRAFFIC FORECASTING
ICLR 2018
- Yaguang Li, et al.
- [Code]
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DEEP TEMPORAL CLUSTERING: FULLY UNSUPERVISED LEARNING OF TIME-DOMAIN FEATURES
- Naveen Sai Madiraju, et al.
- [Code-unofficial implementation ]
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Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
- Guokun Lai, Wei-Cheng Chang, Yiming Yang, Hanxiao Liu
- [Code]
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Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks
NeurIPS 2018
- Bryan Lim. et al.
- Code
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A Memory-Network Based Solution for Multivariate Time-Series Forecasting
- Yen-Yu Chang, et al.
- Code-unofficial implementation]
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Deep learning with long short-term memory networks for financial market predictions
- Fischer, Thomas and Krauss, Christopher.
- Code not yet.
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Discriminative State-Space Models
NIPS 2017
- Vitaly Kuznetsov and Mehryar Mohri.
- Code not yet.
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Hybrid Neural Networks for Learning the Trend in Time Seriesreview
- Tao Lin, et al.
- Code not yet.
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- Slawek Smyl and Karthik Kuber
- Code not yet.
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Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction
NIPS 2016
- Hsiang-Fu Yu, et al.
- [Code]
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Time Series Prediction and Online Learning
JMLR 2016
- Vitaly Kuznetsov and Mehryar Mohri.
- Code not yet.
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Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500
- Krauss, Christopher, et al.
- Code not yet.
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Forecasting economic and financial time series: ARIMA VS. LSTM
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A comparative study between LSTM and ARIMA for sales forecasting in retail
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ARIMA/SARIMA vs LSTM with Ensemble learning Insights for Time Series Data
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Time Series Forecasting Best Practices & Examples from Microsoft
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Stock Market Prediction by Recurrent Neural Network on LSTM Model
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Decoupling Hierarchical Recurrent Neural Networks With Locally Computable Losses
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Forecasting: Principles and Practice: SlidesGood material
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DeepSeries: Deep Learning Models for time series prediction.
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varstan: An R package for Bayesian analysis of structured time series models with Stan
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Deep4cast: Forecasting for Decision Making under Uncertainty
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fireTS: sklean style package for multi-variate time-series prediction.
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EpiSoon: Forecasting the effective reproduction number over short timescales
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Electric Load Forecasting: Load forecasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models.
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TimeseriesAI: Practical Deep Learning for Time Series / Sequential Data using fastai/ Pytorch.
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TimescaleDB: An open-source time-series SQL database optimized for fast ingest and complex queries. Packaged as a PostgreSQL extension.
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Using attentive neural processes for forecasting power usage
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https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting
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pytorch-forecasting: A Python package for time series forecasting with PyTorch. It includes state-of-the-art network architectures