deep learning time series
1.0.0
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...
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
Long Range Probabilistic Forecasting in Time-Series using High Order Statistics
Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Networks
End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series
Neural basis expansion analysis with exogenous variables:Forecasting electricity prices with NBEATSx
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
Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management
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 GmbHInter-Series Attention Model for COVID-19 Forecasting Good reference
MODEL SELECTION IN RECONCILING HIERARCHICAL TIME SERIES
A Strong Baseline for Weekly Time Series Forecasting
Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning
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
A Time-Series Forecasting Performance Comparison for Neural Networks with State Space and ARIMA Models
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
LAVARNET: Neural network modeling of causal variable relationships for multivariate time series forecasting
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
Short-term forecasting of the COVID-19 pandemic using Google Trends data: Evidence from 158 countries
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
Forecasting Economic Recession through Share Price in the Logistics Industry with Artificial Intelligence (AI)
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 FinancePrediction of hierarchical time series using structured regularization and its application to artificial neural networks
Cold-Start Promotional Sales Forecasting through Gradient Boosted-based Contrastive Explanations
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
Amazon ResearchDemand 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
Time Series Regression
Forecasting Supplier Delivery Performance with Recurrent Neural Networks
Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments
Resilient Neural Forecasting Systems
Amazon ResearchDynamic Neural Relational Inference for Forecasting Trajectories
CVPR 2020Traffic 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
NeurocomputingA Novel LSTM for Multivariate Time Series with Massive Missingness
N-BEATS: NEURAL BASIS EXPANSION ANALYSIS FOR INTERPRETABLE TIME SERIES FORECASTING ICLR 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
Forecasting the Short-Term Metro Ridership With Seasonal and Trend Decomposition Using Loess and LSTM Neural Networks
The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models
AWS AI LabsFORECASTING WITH SKTIME: DESIGNING SKTIME’S NEW FORECASTING API AND APPLYING IT TO REPLICATE AND EXTEND THE M4 STUDY
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
G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimes
IBM research and MITDeep 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 2020meta-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
ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting
Anomaly detection for Cybersecurity: time series forecasting and deep learningGood review about forecasting
Event-Driven Continuous Time Bayesian Networks
Research AI, IBMJoint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing ValuesAAAI 2020
IBM Research, NYTopology-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 competitionThink Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series ForecastingNeurIPS 2019
AmazonDeep Landscape Forecasting for Real-time Bidding Advertising KDD 2019
Similarity Preserving Representation Learning for Time Series Clustering
IBM researchDSANet: 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 ResearchDeep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models
Deep Physiological State Space Model for Clinical Forecasting
AR-Net: A simple Auto-Regressive Neural Network for time-series
Facebook ResearchLearning Time-series Data of Industrial Design Optimization using Recurrent Neural Networks
Honda Research Institute Europe GmbHRobustSTL: 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
LHCnn: A Novel Efficient Multivariate Time Series Prediction Framework Utilizing Convolutional Neural Networks
SKTIME: A UNIFIED INTERFACE FOR MACHINE LEARNING WITH TIME SERIE
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions
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
Explainable Deep Neural Networks for Multivariate Time Series Predictions IJCAI 2019
IBM Research, ZurichOutlier 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
Data Preprocessing and Augmentation for Multiple Short Time Series Forecasting with Recurrent Neural Networks
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
Machine learning
Artificial intelligence
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
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
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
