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...
Neural basis expansion analysis with exogenous variables:Forecasting electricity prices with NBEATSx
Kin G. Olivares, et al.
Code not yet.
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting reference
An Experimental Review on Deep Learning Architectures for Time Series Forecasting
Long Horizon Forecasting With Temporal Point Processes
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting AAAI 2021
CHALLENGES AND APPROACHES TO TIME-SERIES FORECASTING IN DATA CENTER TELEMETRY: A SURVEY
Physics-constrained Deep Recurrent Neural Models of Building Thermal Dynamics
MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification
Learning to Select the Best Forecasting Tasks for Clinical Outcome Prediction
Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning
Honda Research Institute Europe GmbH
Inter-Series Attention Model for COVID-19 Forecasting Good reference
MODEL SELECTION IN RECONCILING HIERARCHICAL TIME SERIES
A Strong Baseline for Weekly Time Series Forecasting
Modeling Heterogeneous Seasonality With Recurrent Neural Networks Using IoT Time Series Data for Defrost Detection and Anomaly Analysis Good Reference
An Examination of the State-of-the-Art for Multivariate Time Series Classification
Rank Position Forecasting in Car Racing
Mixed Membership Recurrent Neural Networks for Modeling Customer Purchases
An analysis of deep neural networks for predicting trends in time series data
Automatic Forecasting using Gaussian Processes
Attention based Multi-Modal New Product Sales Time-series Forecasting
Demand Forecasting of individual Probability Density Functions with Machine Learning
Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature
Multivariate Time-series Anomaly Detection via Graph Attention Network
Graph Neural Networks for Model Recommendation using Time Series Data
Kaggle forecasting competitions: An overlooked learning opportunity
Forecasting with Multiple Seasonality
Forecasting Hierarchical Time Series with a Regularized Embedding Space
Forecasting the Evolution of Hydropower Generation
Deep State-Space Generative Model For Correlated Time-to-Event Predictions
Scalable Low-Rank Autoregressive Tensor Learning for Spatiotemporal Traffic Data Imputation
clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series
Speed Anomalies and Safe Departure Times from Uber Movement Data
Forecasting AI Progress: A Research Agenda
Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation
Interpretable Sequence Learning for COVID-19 Forecasting
Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records meta-learning
PRINCIPLES AND ALGORITHMS FOR FORECASTING GROUPS OF TIME SERIES: LOCALITY AND GLOBALITY
Multi-stream RNN for Merchant Transaction Prediction
KDD 2020 Workshop on Machine Learning in Finance
Cold-Start Promotional Sales Forecasting through Gradient Boosted-based Contrastive Explanations
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
Amazon Research
Demand Forecasting in the Presence of Privileged Information
Seasonal Self-evolving Neural Networks Based Short-term Wind Farm Generation Forecast
Distributed ARIMA Models for Ultra-long Time Series Spark
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction LSTM application
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
Dynamic Multi-Scale Convolutional Neural Network for Time Series Classification
Neural Architecture Search for Time Series Classification
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions
Forecasting Supplier Delivery Performance with Recurrent Neural Networks
Resilient Neural Forecasting Systems
Amazon Research
Dynamic Neural Relational Inference for Forecasting Trajectories
CVPR 2020
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting
Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series
Neuroevolution Strategy for Time Series Prediction
COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
A machine learning approach for forecasting hierarchical time series
ProbCast: Open-source Production, Evaluation and Visualisation of Probabilistic Forecasts
Exploring Clinical Time Series Forecasting with Meta-Features in Variational Recurrent Modelsmeta-learning
Semisupervised Deep State-Space Model for Plant Growth Modeling
EFFECTIVE AND EFFICIENT COMPUTATION WITH MULTIPLE-TIMESCALE SPIKING RECURRENT NEURAL NETWORKS
Multivariate time series forecasting via attention-based encoder–decoder framework
Neurocomputing
A Novel LSTM for Multivariate Time Series with Massive Missingness
N-BEATS: NEURAL BASIS EXPANSION ANALYSIS FOR INTERPRETABLE TIME SERIES FORECASTINGICLR 2020
How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecastinggood new approach
The Hybrid Forecasting Method SVR-ESAR for Covid-19
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models
AWS AI Labs
LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns
A NETWORK-BASED TRANSFER LEARNING APPROACH TO IMPROVE SALES FORECASTING OF NEW PRODUCTS
DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting Good new approach
An Approach for Complex Event Streams Processing and Forecasting
Knowledge Enhanced Neural Fashion Trend Forecasting
Augmented Out-of-Sample Comparison Method for Time Series Forecasting Techniques
Enhancing High Frequency Technical Indicators Forecasting Using Shrinking Deep Neural Networks ICIM 2020
Time Series Forecasting With Deep Learning: A Survey Good summary
Neural forecasting: Introduction and literature overview
Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories
Orbit: Probabilistic Forecast with Exponential Smoothing
Daily retail demand forecasting using machine learning with emphasis on calendric special days
FORECASTING IN MULTIVARIATE IRREGULARLY SAMPLED TIME SERIES WITH MISSING VALUES
Multi-label Prediction in Time Series Data using Deep Neural Networks
TraDE: Transformers for Density Estimation
Deep Probabilistic Modelling of Price Movements for High-Frequency Trading
Deep State Space Models for Nonlinear System Identification
Zero-shot and few-shot time series forecasting with ordinal regression recurrent neural networks
Financial Time Series Representation Learning
IBM research and MIT
Deep Markov Spatio-Temporal Factorization
Harmonic Recurrent Process for Time Series Forecasting
Elastic Machine Learning Algorithms in Amazon SageMaker
Time Series Data Augmentation for Deep Learning: A Survey
Block Hankel Tensor ARIMA for Multiple Short Time Series ForecastingAAAI 2020
meta-learning
Learnings from Kaggle's Forecasting Competitions
An Industry Case of Large-Scale Demand Forecasting of Hierarchical Components
Multi-variate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows
Anomaly detection for Cybersecurity: time series forecasting and deep learningGood review about forecasting
Event-Driven Continuous Time Bayesian Networks
Research AI, IBM
IBM Research, NY
Topology-Based Clusterwise Regression for User Segmentation and Demand Forecasting
Evolutionary LSTM-FCN networks for pattern classification in industrial processes
Forecasting Multivariate Time-Series Data Using LSTM and Mini-Batches
Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time SeriesAAAI 2020
RELATIONAL STATE-SPACE MODEL FOR STOCHASTIC MULTI-OBJECT SYSTEMSICLR 2020
For2For: Learning to forecast from forecasts
Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning AAAI 2020
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting Reference
Forecasting Big Time Series: Theory and PracticeKDD 2019
Relevant tutorial
Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting
A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
Winning submission of the M4 forecasting competition
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series ForecastingNeurIPS 2019
Amazon
Deep Landscape Forecasting for Real-time Bidding Advertising KDD 2019
Similarity Preserving Representation Learning for Time Series Clustering
IBM research
DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting
Enhancing Time Series Momentum Strategies Using Deep Neural Networks
DYNAMIC TIME LAG REGRESSION: PREDICTING WHAT & WHEN
Time-series Generative Adversarial NetworksNeurIPS 2019
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting
Google Research
Deep Physiological State Space Model for Clinical Forecasting
AR-Net: A simple Auto-Regressive Neural Network for time-series
Facebook Research
Learning Time-series Data of Industrial Design Optimization using Recurrent Neural Networks
Honda Research Institute Europe GmbH
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Constructing Gradient Controllable Recurrent Neural Networks Using Hamiltonian Dynamics
SOM-VAE: Interpretable Discrete Representation Learning on Time SeriesICLR 2019
Unsupervised Scalable Representation Learning for Multivariate Time SeriesNeurIPS 2019
In Applications -- Time Series Analysis
Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series
You May Not Need Order in Time Series Forecasting
Shape and Time Distortion Loss for Training Deep Time Series Forecasting ModelsNeurIPS2019
Dynamic Local Regret for Non-convex Online ForecastingNeurIPS 2019
Bayesian Temporal Factorization for Multidimensional Time Series Prediction
Probabilistic sequential matrix factorization
Sequential VAE-LSTM for Anomaly Detection on Time Series
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula ProcessesNeurIPS 2019
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
SKTIME: A UNIFIED INTERFACE FOR MACHINE LEARNING WITH TIME SERIE
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
Explainable Deep Neural Networks for Multivariate Time Series Predictions IJCAI 2019
IBM Research, Zurich
Outlier Detection for Time Series with Recurrent Autoencoder Ensembles IJCAI 2019
Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting IJCAI 2019
Deep Factors for Forecasting ICML 2019
Probabilistic Forecasting with Spline Quantile Function RNNs
Deep learning for time series classification: a review
Multivariate LSTM-FCNs for Time Series Classification
Criteria for classifying forecasting methods
GluonTS: Probabilistic Time Series Models in Python
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
Statistical and Machine Learning forecasting methods: Concerns and ways forward
Attend and Diagnose: Clinical Time Series Analysis Using Attention Models AAAI 2018
Precision and Recall for Time Series NeurIPS2018
Deep State Space Models for Time Series Forecasting NeurIPS2018
Deep Factors with Gaussian Processes for Forecasting
Third workshop on Bayesian Deep Learning (NeurIPS 2018)
DIFFUSION CONVOLUTIONAL RECURRENT NEURAL NETWORK: DATA-DRIVEN TRAFFIC FORECASTINGICLR 2018
DEEP TEMPORAL CLUSTERING: FULLY UNSUPERVISED LEARNING OF TIME-DOMAIN FEATURES
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks NeurIPS 2018
A Memory-Network Based Solution for Multivariate Time-Series Forecasting
Deep learning with long short-term memory networks for financial market predictions
Discriminative State-Space ModelsNIPS 2017
Hybrid Neural Networks for Learning the Trend in Time Seriesreview
Temporal Regularized Matrix Factorization for High-dimensional Time Series PredictionNIPS 2016
Time Series Prediction and Online LearningJMLR 2016
Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500
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
Time Series Forecasting Best Practices & Examples from Microsoft
Stock Market Prediction by Recurrent Neural Network on LSTM Model
Decoupling Hierarchical Recurrent Neural Networks With Locally Computable Losses
Forecasting: Principles and Practice: SlidesGood material
DeepSeries: Deep Learning Models for time series prediction.
varstan: An R package for Bayesian analysis of structured time series models with Stan
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.
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.
Using attentive neural processes for forecasting power usage
https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting
pytorch-forecasting: A Python package for time series forecasting with PyTorch. It includes state-of-the-art network architectures