mlsanityData to 7 Oct 2026

Monash Time Series Forecasting Archive

arXiv 2105.06643 ↗First posted 14 May 2021cs.LG stat.MLcited by 256

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Abstract (arXiv)

Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area. Global forecasting models that are trained across sets of time series have shown a huge potential in providing accurate forecasts compared with the traditional univariate forecasting models that work on isolated series. However, there are currently no comprehensive time series archives for forecasting that contain datasets of time series from similar sources available for the research community to evaluate the performance of new global forecasting algorithms over a wide variety of datasets. In this paper, we present such a comprehensive time series forecasting archive containing 20 publicly available time series datasets from varied domains, with different characteristics in terms of frequency, series lengths, and inclusion of missing values. We also characterise the datasets, and identify similarities and differences among them, by conducting a feature analysis. Furthermore, we present the performance of a set of standard baseline forecasting methods over all datasets across eight error metrics, for the benefit of researchers using the archive to benchmark their forecasting algorithms.

Key facts. Cited by 256 papers in the map (data through 7 Oct 2026). Builds on 25 of its 136 references that are in the map. First posted on arXiv 14 May 2021.

Builds on

25 of 136 references.

References · 25

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  3. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  4. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  5. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  6. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
  7. 2018The UCR Time Series Archivecited by 421
  8. 2018The UEA multivariate time series classification archive, 2018cited by 330
  9. 2017A Multi-Horizon Quantile Recurrent Forecastercited by 211
  10. 2019Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directionscited by 172
  11. 2019Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecastingcited by 161
  12. 2019High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processescited by 147
13 more
  1. 2017Conditional Time Series Forecasting with Convolutional Neural Networkscited by 121
  2. 2019catch22: CAnonical Time-series CHaracteristicscited by 103
  3. 2019sktime: A Unified Interface for Machine Learning with Time Seriescited by 98
  4. 2020Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globalitycited by 70
  5. 2020Kaggle forecasting competitions: An overlooked learning opportunitycited by 55
  6. 2019LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patternscited by 23
  7. 2020Ensembles of Localised Models for Time Series Forecastingcited by 20
  8. 2018Foundations of Sequence-to-Sequence Modeling for Time Seriescited by 15
  9. 2016Automatic time-series phenotyping using massive feature extractioncited by 12
  10. 2020Monash University, UEA, UCR Time Series Extrinsic Regression Archivecited by 10
  11. 2020An Accurate and Fully-Automated Ensemble Model for Weekly Time Series Forecastingcited by 4
  12. 2019Predicting Electricity Consumption using Deep Recurrent Neural Networkscited by 1
  13. 2019A Single Scalable LSTM Model for Short-Term Forecasting of Disaggregated Electricity Loadscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 15; 2023: 23; 2024: 66; 2025: 66; 2026: 82.

256 papers.

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  1. 2024Chronos: Learning the Language of Time Seriescited by 712
  2. 2023A decoder-only foundation model for time-series forecastingcited by 650
  3. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  4. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
  5. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 453
  6. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 280
  7. 2024TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methodscited by 242
  8. 2024Timer: Generative Pre-trained Transformers Are Large Time Series Modelscited by 198
  9. 2024GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluationcited by 188
  10. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 163
  11. 2025Chronos-2: From Univariate to Universal Forecastingcited by 158
  12. 2024Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Seriescited by 120
38 more
  1. 2023Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecastingcited by 102
  2. 2024UniTS: A Unified Multi-Task Time Series Modelcited by 93
  3. 2024ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Datacited by 92
  4. 2024Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Expertscited by 88
  5. 2021Chaos as an interpretable benchmark for forecasting and data-driven modellingcited by 87
  6. 2024VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasterscited by 87
  7. 2023Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecastingcited by 86
  8. 2025This Time is Different: An Observability Perspective on Time Series Foundation Modelscited by 57
  9. 2024LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Promptingcited by 56
  10. 2023Effectively Modeling Time Series with Simple Discrete State Spacescited by 54
  11. 2025fev-bench: A Realistic Benchmark for Time Series Forecastingcited by 52
  12. 2024Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Reviewcited by 50
  13. 2024Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilitiescited by 48
  14. 2021Long-Range Transformers for Dynamic Spatiotemporal Forecastingcited by 46
  15. 2025DBLoss: Decomposition-based Loss Function for Time Series Forecastingcited by 44
  16. 2024Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecastingcited by 40
  17. 2024Retrieval Augmented Time Series Forecastingcited by 40
  18. 2022TACTiS: Transformer-Attentional Copulas for Time Seriescited by 38
  19. 2022Deep Latent State Space Models for Time-Series Generationcited by 37
  20. 2024TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysiscited by 37
  21. 2024Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoderscited by 37
  22. 2023AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecastingcited by 36
  23. 2022Koopman Neural Forecaster for Time Series with Temporal Distribution Shiftscited by 35
  24. 2025Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspectivecited by 34
  25. 2024TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Modelcited by 33
  26. 2026AIRS-Bench: a Suite of Tasks for Frontier AI Research Science Agentscited by 32
  27. 2024Towards a General Time Series Forecasting Model with Unified Representation and Adaptive Transfercited by 31
  28. 2024In-Context Fine-Tuning for Time-Series Foundation Modelscited by 31
  29. 2022GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalizationcited by 28
  30. 2024Universal Time-Series Representation Learning: A Surveycited by 28
  31. 2025LightGTS: A Lightweight General Time Series Forecasting Modelcited by 28
  32. 2025Aurora: Towards Universal Generative Multimodal Time Series Forecastingcited by 28
  33. 2023Model scale versus domain knowledge in statistical forecasting of chaotic systemscited by 27
  34. 2024Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detectioncited by 26
  35. 2023TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Seriescited by 25
  36. 2024Zero-shot forecasting of chaotic systemscited by 25
  37. 2026It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarkscited by 25
  38. 2023Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domaincited by 23

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