List of state of the art papers, code, and other resources focus on time series forecasting.
TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting Arxiv
UniTS: Building a Unified Time Series Model Arxiv
Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting Arxiv
MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting ICLR 2023 Oral
Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting ICLR 2023
SAITS: Self-Attention-based Imputation for Time Series Expert Systems with Applications
A Time Series is Worth 64 Words: Long-term Forecasting with Transformers ICLR 2023
[Deep Learning for Time Series Anomaly Detection: A Survey]() survey
[A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting]() survey
[Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting]() NeurIPS 2022
[Generative Time Series Forecasting with Diffusion, Denoise and Disentanglement]() NeurIPS 2022
[SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction]() NeurIPS 2022
[Learning Latent Seasonal-Trend Representations for Time Series Forecasting]() NeurIPS 2022
[GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks]() NeurIPS 2022
[Causal Disentanglement for Time Series]() NeurIPS 2022
[Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency]() NeurIPS 2022
[FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting]() NeurIPS 2022
[BILCO: An Efficient Algorithm for Joint Alignment of Time Series]() NeurIPS 2022
[LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data]() NeurIPS 2022
[Unsupervised Learning of Algebraic Structure from Stationary Time Sequences]() NeurIPS 2022
[Dynamic Sparse Network for Time Series Classification: Learning What to “See”]() NeurIPS 2022
[WaveBound: Dynamically Bounding Error for Stable Time Series Forecasting]() NeurIPS 2022
[Conditional Loss and Deep Euler Scheme for Time Series Generation]() AAAI 2022
[I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series
Analysis and Embedding]() AAAI 2022
[TS2Vec: Towards Universal Representation of Time Series]() AAAI 2022
[Reinforcement Learning based Dynamic Model Combination for Time Series Forecasting]() AAAI 2022
[CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting]() AAAI 2022
Transformers in Time Series: A Survey review
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting ICLR 2022 oral
A machine learning approach for forecasting hierarchical time series
Probabilistic Transformer For Time Series Analysis NeuIPS 2021
Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting NeuIPS 2021
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation NeuIPS 2021
Variational Inference for Continuous-Time Switching Dynamical Systems NeuIPS 2021
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data NeuIPS 2021
Coresets for Time Series Clustering NeuIPS 2021
Online false discovery rate control for anomaly detection in time series NeuIPS 2021
Adjusting for Autocorrelated Errors in Neural Networks for Time Series NeuIPS 2021
Deep Explicit Duration Switching Models for Time Series NeuIPS 2021
Deep Learning for Time Series Forecasting: A Survey survey
Whittle Networks: A Deep Likelihood Model for Time Series ICML 2021
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting ICML 2021
Long Horizon Forecasting With Temporal Point Processes WSDM 2021
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting AAAI 2021 best paper
Coupled Layer-wise Graph Convolution for Transportation Demand Prediction AAAI 2021
Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting AAAI 2020
Adversarial Sparse Transformer for Time Series Forecasting NeurIPS 2020
Benchmarking Deep Learning Interpretability in Time Series Predictions NeurIPS 2020
Deep reconstruction of strange attractors from time series NeurIPS 2020
Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline classification
Active Model Selection for Positive Unlabeled Time Series Classification
Unsupervised Phase Learning and Extraction from Quasiperiodic Multidimensional Time-series Data
Connecting the Dots: Multivariate Time Series Forecasting withGraph Neural Networks
RobustTAD: Robust Time Series Anomaly Detection viaDecomposition and Convolutional Neural Networks
Neural Controlled Differential Equations forIrregular Time Series
University of Oxford
Time Series Forecasting With Deep Learning: A Survey
Neural forecasting: Introduction and literature overview
Amazon Research
Time Series Data Augmentation for Deep Learning: A Survey
Modeling time series when some observations are zeroJournal of Econometrics 2020
Meta-learning framework with applications to zero-shot time-series forecasting
Harmonic Recurrent Process for Time Series Forecasting
Block Hankel Tensor ARIMA for Multiple Short Time Series ForecastingAAAI 2020
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
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
Seglearn: A Python Package for Learning Sequences and Time Series
tsflex: Flexible Time Series Processing & Feature Extraction
PyTorch Forecasting: A Python Package for time series forecasting with PyTorch
HyperTS: A Full-Pipeline Automated Time Series Analysis Toolkit
List of tools & datasets for anomaly detection on time-series data
A scikit-learn compatible Python toolbox for machine learning with time series
plotly-resampler: Visualize large time series data with plotly.py
A statistical library designed to fill the void in Python's time series analysis capabilities
RNN based Time-series Anomaly detector model implemented in Pytorch
A Python toolkit for rule-based/unsupervised anomaly detection in time series
A curated list of awesome time series databases, benchmarks and papers
Matrix Profile analysis methods in Python for clustering, pattern mining, and anomaly detection