Awesome Time Series

Awesome Time Series

List of state of the art papers, code, and other resources focus on time series forecasting.

Table of Contents

• M4 competition
• Kaggle time series competition
• Papers
• Conferences
• Theory-Resource
• Code Resource
• Datasets

M4-competition

M4

papers

• The M4 Competition: 100,000 time series and 61 forecasting methods
• A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting
• Weighted ensemble of statistical models
• FFORMA: Feature-based forecast model averaging

Kaggle-time-series-competition

• Walmart Store Sales Forecasting (2014)
• Walmart Sales in Stormy Weather (2015)
• Rossmann Store Sales (2015)
• Wikipedia Web Traffic Forecasting (2017)
• Corporación Favorita Grocery Sales Forecasting (2018)
• Recruit Restaurant Visitor Forecasting (2018)
• COVID19 Global Forecasting (2020)
• Jane Street Future Market Prediction(2021)

Papers

2022

• Deep Learning for Time Series Anomaly Detection: A Survey survey
• A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting survey
  • [code]
• 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
  • Wen, et al.
  • Code
• Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting ICLR 2022 oral
  • Liu, et al.

2021

• A machine learning approach for forecasting hierarchical time series
  • Mancuso, et al.
• Probabilistic Transformer For Time Series Analysis NeuIPS 2021
  • Tang, et al.
• Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting NeuIPS 2021
  • Wu, et al.
• CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation NeuIPS 2021
  • Yusuke, et al.
• Variational Inference for Continuous-Time Switching Dynamical Systems NeuIPS 2021
  • Lukas, et al.
• MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data NeuIPS 2021
  • Zhu, et al.
• Coresets for Time Series Clustering NeuIPS 2021
  • Zhou, et al.
• Online false discovery rate control for anomaly detection in time series NeuIPS 2021
  • Quentin, et al.
• Adjusting for Autocorrelated Errors in Neural Networks for Time Series NeuIPS 2021
  • Sun, et al.
• Deep Explicit Duration Switching Models for Time Series NeuIPS 2021
  • Zhou, et al.
• Deep Learning for Time Series Forecasting: A Survey survey
  • Torres, et al.
• Whittle Networks: A Deep Likelihood Model for Time Series ICML 2021
  • Yu, et al.
  • Code
• Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting ICML 2021
  • Chen, et al.
  • Code
• Long Horizon Forecasting With Temporal Point Processes WSDM 2021
  • Deshpande, et al.
  • Code
• Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting AAAI 2021 best paper
  • Zhou, et al.
  • Code
• Coupled Layer-wise Graph Convolution for Transportation Demand Prediction AAAI 2021
  • Ye, et al.
  • Code

2020

• Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting AAAI 2020
  • Shi, et al.
  • Code
• Adversarial Sparse Transformer for Time Series Forecasting NeurIPS 2020
  • Wu, et al.
  • Code not yet
• Benchmarking Deep Learning Interpretability in Time Series Predictions NeurIPS 2020
  • Ismail, et al.
  • [Code]
• Deep reconstruction of strange attractors from time series NeurIPS 2020
  • Gilpin, et al.
  • [Code]
• Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline classification
  • Tang, et al.
  • [Code]
• Active Model Selection for Positive Unlabeled Time Series Classification
  • Liang, et al.
  • [Code]
• Unsupervised Phase Learning and Extraction from Quasiperiodic Multidimensional Time-series Data
  • Prayook, et al.
  • [Code]
• Connecting the Dots: Multivariate Time Series Forecasting withGraph Neural Networks
  • Wu, et al.
  • [Code]
• Forecasting with sktime: Designing sktime's New Forecasting API and Applying It to Replicate and Extend the M4 Study
  • Löning, et al.
  • Code not yet
• RobustTAD: Robust Time Series Anomaly Detection viaDecomposition and Convolutional Neural Networks
  • Gao, et al.
  • Code not yet
• Neural Controlled Differential Equations forIrregular Time Series
  • Patrick Kidger, et al.
  • University of Oxford
  • [Code]
• Time Series Forecasting With Deep Learning: A Survey
  • Lim, et al.
  • Code not yet
• Neural forecasting: Introduction and literature overview
  • Benidis, et al.
  • Amazon Research
  • Code not yet.
• Time Series Data Augmentation for Deep Learning: A Survey
  • Wen, et al.
  • Code not yet
• Modeling time series when some observations are zeroJournal of Econometrics 2020
  • Andrew Harveyand Ryoko Ito.
  • Code not yet
• Meta-learning framework with applications to zero-shot time-series forecasting
  • Oreshkin, et al.
  • Code not yet.
• Harmonic Recurrent Process for Time Series Forecasting
  • Shao-Qun Zhang and Zhi-Hua Zhou.
  • Code not yet.
• Block Hankel Tensor ARIMA for Multiple Short Time Series ForecastingAAAI 2020
  • QIQUAN SHI, et al.
  • Code not yet
• 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 not yet.
• 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.

Conferences

• ICLR
• AAAI
• IJCAI
• ISF
• NeurIPS
• ICML
• M5 Competition

Theory-Resource

• Time Series Analysis, MIT
• Time Series Forecasting, Udacity
• Practical Time Series Analysis, Cousera
• Sequences, Time Series and Prediction
• Intro to Time Series Analysis in R, Cousera
• Anomaly Detection in Time Series Data with Keras, Corsera
• Applying Data Analytics in Finance, Coursera
• Time Series Forecasting using Python
• STAT 510: Applied Time Series Analysis, PSU
• Policy Analysis Using Interrupted Time Series, edx
• Time Series Forecasting in Python
• time-series-transformers-review

Code-Resource

• FOST from microsoft
• pyWATTS: Python Workflow Automation Tool for Time-Series
• Seglearn: A Python Package for Learning Sequences and Time Series
• cesium: Open-Source Platform for Time Series Inference
• PyTorch Forecasting: A Python Package for time series forecasting with PyTorch
• A collection of time series prediction methods: rnn, seq2seq, cnn, wavenet, transformer, unet, n-beats, gan, kalman-filter
• Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series
• Predicting/hypothesizing the findings of the M4 Competition
• PyFlux
• Time Series Forecasting Best Practices & Examples
• List of tools & datasets for anomaly detection on time-series data
• python packages for time series analysis
• A scikit-learn compatible Python toolbox for machine learning with time series
• time series visualization tools
• 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
• ARCH models in Python
• 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
• Flow Forecast: A deep learning framework for time series forecasting, classification and anomaly detection built in PyTorch

Datasets

• SkyCam: A Dataset of Sky Images and their Irradiance values
• U.S. Air Pollution Data
• U.S. Chronic Disease Data
• Air quality from UCI
• Seattle freeway traffic speed
• Youth Tobacco Survey Data
• Singapore Population
• Airlines Delay
• Airplane Crashes
• Electricity dataset from UCI
• Traffic dataset from UCI
• City of Baltimore Crime Data
• Discover The Menu
• Global Climate Change Data
• Global Health Nutrition Data
• Beijing PM2.5 Data Set
• Airline Passengers dataset
• Government Finance Statistics
• Historical Public Debt Data
• Kansas City Crime Data
• NYC Crime Data
• Kaggle-Web Traffic Time Series Forecasting

Reference

• cuge1995/awesome-time-series: list of papers, code, and other resources