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

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

  • Kashif Rasul, et al.
  • Code not yet.

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

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

2020

• Long Horizon Forecasting With Temporal Point Processes

  • Prathamesh Deshpande, 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 not yet.

• MODEL SELECTION IN RECONCILING HIERARCHICAL TIME SERIES

  • M. ABOLGHASEMI, et al.
  • Code not yet.

• 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 not yet.

• 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 FORECASTINGICLR 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

2019

• 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

2018

• 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

2017

• 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.

2016

• 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.

Comparative: Classical methods vs Deep Learning methods

• 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

Conferences

• Machine learning

  • NeurIPS
  • ICML
  • ICLR

• Artificial intelligence

  • AAAI
  • AISTATS
  • ICANN
  • IJCAI
  • UAI

Competitions

• M5 Competition
• M4 Competition

Code

• Notebooks
• Code

Theory-Resource

• 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

Code-Resource

• 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

Datasets

• 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