mlsanityData to 14 Oct 2026

Distributed and parallel time series feature extraction for industrial big data applications

arXiv 1610.07717 ↗First posted 25 Oct 2016cs.LGcited by 30

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Abstract (arXiv)

The all-relevant problem of feature selection is the identification of all strongly and weakly relevant attributes. This problem is especially hard to solve for time series classification and regression in industrial applications such as predictive maintenance or production line optimization, for which each label or regression target is associated with several time series and meta-information simultaneously. Here, we are proposing an efficient, scalable feature extraction algorithm for time series, which filters the available features in an early stage of the machine learning pipeline with respect to their significance for the classification or regression task, while controlling the expected percentage of selected but irrelevant features. The proposed algorithm combines established feature extraction methods with a feature importance filter. It has a low computational complexity, allows to start on a problem with only limited domain knowledge available, can be trivially parallelized, is highly scalable and based on well studied non-parametric hypothesis tests. We benchmark our proposed algorithm on all binary classification problems of the UCR time series classification archive as well as time series from a production line optimization project and simulated stochastic processes with underlying qualitative change of dynamics.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Oct 2016.

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Citing papers per year: 2021: 4; 2022: 7; 2023: 6; 2024: 8; 2025: 3; 2026: 2.

30 papers.

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  1. 2022Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time Series Predictioncited by 12
  2. 2022Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Featurescited by 7
  3. 2023Feature Programming for Multivariate Time Series Predictioncited by 7
  4. 2021Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Surveycited by 5
  5. 2022Feature-Based Time-Series Analysis in R using the theft Packagecited by 4
  6. 2022Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavitiescited by 3
  7. 2023Automatic Feature Engineering for Time Series Classification: Evaluation and Discussioncited by 3
  8. 2024A Survey on Time-Series Distance Measurescited by 3
  9. 2025ELATE: Evolutionary Language model for Automated Time-series Engineeringcited by 3
  10. 2021Curvature-based Feature Selection with Application in Classifying Electronic Health Recordscited by 2
  11. 2021AutoFITS: Automatic Feature Engineering for Irregular Time Seriescited by 2
  12. 2022Intrinsic Anomaly Detection for Multi-Variate Time Seriescited by 2
18 more
  1. 2022Improving Accuracy and Explainability of Online Handwriting Recognitioncited by 2
  2. 2025MSAD: A Deep Dive into Model Selection for Time series Anomaly Detectioncited by 2
  3. 2023Automated Machine Learning for Remaining Useful Life Predictionscited by 1
  4. 2023GAME: Generalized deep learning model towards multimodal data integration for early screening of adolescent mental disorderscited by 1
  5. 2024Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videoscited by 1
  6. 2021Local Exceptionality Detection in Time Series Using Subgroup Discoverycited by 0
  7. 2022A Survey of Open Source Automation Tools for Data Science Predictionscited by 0
  8. 2023Distance Rank Score: Unsupervised filter method for feature selection on imbalanced datasetcited by 0
  9. 2023KPIs-Based Clustering and Visualization of HPC jobs: a Feature Reduction Approachcited by 0
  10. 2024A Novel Hybrid Feature Importance and Feature Interaction Detection Framework for Predictive Optimization in Industry 4.0 Applicationscited by 0
  11. 2024Comparison of gait phase detection using traditional machine learning and deep learning techniquescited by 0
  12. 2024Meta-learning and Data Augmentation for Stress Testing Forecasting Modelscited by 0
  13. 2024Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Datacited by 0
  14. 2024Optimal starting point for time series forecastingcited by 0
  15. 2024Machine learning-based classification for Single Photon Space Debris Light Curvescited by 0
  16. 2025On Multivariate Financial Time Series Classificationcited by 0
  17. 2026Statistical comparisons of time-series feature sets on classification taskscited by 0
  18. 2026Automated Feature Engineering, AutoML, and Decision-Focused Learning for Improved Energy Consumption Forecastingcited by 0