Deep Learning Time Series Forecasting

List of state of the art papers focus on deep learning and resources, code and experiments using deep learning for time series forecasting. Classic methods vs Deep Learning methods, Competitions...

Table of Contents

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

Papers

2021

• 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]

2020

• 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 thr