mlsanityData to 7 Oct 2026

GRATIS: GeneRAting TIme Series with diverse and controllable characteristics

arXiv 1903.02787 ↗First posted 7 Mar 2019stat.ML cs.LG stat.COcited by 42

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Abstract (arXiv)

The explosion of time series data in recent years has brought a flourish of new time series analysis methods, for forecasting, clustering, classification and other tasks. The evaluation of these new methods requires either collecting or simulating a diverse set of time series benchmarking data to enable reliable comparisons against alternative approaches. We propose GeneRAting TIme Series with diverse and controllable characteristics, named GRATIS, with the use of mixture autoregressive (MAR) models. We simulate sets of time series using MAR models and investigate the diversity and coverage of the generated time series in a time series feature space. By tuning the parameters of the MAR models, GRATIS is also able to efficiently generate new time series with controllable features. In general, as a costless surrogate to the traditional data collection approach, GRATIS can be used as an evaluation tool for tasks such as time series forecasting and classification. We illustrate the usefulness of our time series generation process through a time series forecasting application.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Mar 2019.

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Citing papers per year: 2021: 7; 2022: 11; 2023: 9; 2024: 8; 2025: 3; 2026: 4.

42 papers.

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  1. 2024A Comprehensive Survey on Data Augmentationcited by 38
  2. 2023CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discoverycited by 35
  3. 2022Forecast combinations: an over 50-year reviewcited by 34
  4. 2022Controllable Data Generation by Deep Learning: A Reviewcited by 20
  5. 2022A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecastingcited by 12
  6. 2021Deep Autoregressive Models with Spectral Attentioncited by 9
  7. 2023Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Surveycited by 9
  8. 2021Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scalecited by 7
  9. 2023Towards Diverse and Coherent Augmentation for Time-Series Forecastingcited by 7
  10. 2021Novel Features for Time Series Analysis: A Complex Networks Approachcited by 6
  11. 2022Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Datacited by 5
  12. 2022What is the best RNN-cell structure to forecast each time series behavior?cited by 5
30 more
  1. 2022Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasetscited by 3
  2. 2023Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challengecited by 3
  3. 2024Data Augmentation for Multivariate Time Series Classification: An Experimental Studycited by 3
  4. 2021Exploring the representativeness of the M5 competition datacited by 2
  5. 2023The Bayesian Context Trees State Space Model for time series modelling and forecastingcited by 2
  6. 2024RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithmscited by 2
  7. 2026FinStressTS: A Parametric Synthetic Benchmark for Time-Series Forecasting in Financecited by 2
  8. 2022Don't overfit the history -- Recursive time series data augmentationcited by 1
  9. 2023Gait Data Augmentation using Physics-Based Biomechanical Simulationcited by 1
  10. 2024Review of Data-centric Time Series Analysis from Sample, Feature, and Periodcited by 1
  11. 2025L-GTA: Latent Generative Modeling for Time Series Augmentationcited by 1
  12. 2021The mbsts package: Multivariate Bayesian Structural Time Series Models in Rcited by 0
  13. 2021Personalized Online Machine Learningcited by 0
  14. 2021Hydroclimatic time series features at multiple time scalescited by 0
  15. 2022A Review of Open Source Software Tools for Time Series Analysiscited by 0
  16. 2022Features of the Earth's seasonal hydroclimate: Characterizations and comparisons across the Koppen-Geiger climates and across continentscited by 0
  17. 2022Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plotscited by 0
  18. 2022fETSmcs: Feature-based ETS model component selectioncited by 0
  19. 2023Generative models for wearables datacited by 0
  20. 2023Forecasting large collections of time series: feature-based methodscited by 0
  21. 2023Enhancing Algorithm Performance Understanding through tsMorph: Generating Semi-Synthetic Time Series for Robust Forecasting Evaluationcited by 0
  22. 2024Fractal interpolation in the context of prediction accuracy optimizationcited by 0
  23. 2024EXPRTS: Exploring and Probing the Robustness of Time Series Forecasting Modelscited by 0
  24. 2024Meta-learning and Data Augmentation for Stress Testing Forecasting Modelscited by 0
  25. 2024Optimal starting point for time series forecastingcited by 0
  26. 2025Towards Time Series Generation Conditioned on Unstructured Natural Languagecited by 0
  27. 2025Data-Driven Analysis of Crash Patterns in SAE Level 2 and Level 4 Automated Vehicles Using K-means Clustering and Association Rule Miningcited by 0
  28. 2026Synthetic Time Series Generation via Complex Networkscited by 0
  29. 2026Preserving Temporal Dynamics in Time Series Generationcited by 0
  30. 2026The Simulacrum: Decision-Theoretic Pretraining for Near-Optimal Time-Series Forecasting and Inferencecited by 0