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

arXiv: https://arxiv.org/abs/1610.07717 · HTML: https://mlsanity.com/paper/1610.07717/

## Key facts

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

## Built on by (most cited first, 30 of 30)

- [Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time Series Prediction](https://mlsanity.com/paper/2203.00971/) (2022)
- [Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features](https://mlsanity.com/paper/2207.04307/) (2022)
- [Feature Programming for Multivariate Time Series Prediction](https://mlsanity.com/paper/2306.06252/) (2023)
- [Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Survey](https://mlsanity.com/paper/2102.07969/) (2021)
- [Feature-Based Time-Series Analysis in R using the theft Package](https://mlsanity.com/paper/2208.06146/) (2022)
- [Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities](https://mlsanity.com/paper/2202.05610/) (2022)
- [Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion](https://mlsanity.com/paper/2308.01071/) (2023)
- [A Survey on Time-Series Distance Measures](https://mlsanity.com/paper/2412.20574/) (2024)
- [ELATE: Evolutionary Language model for Automated Time-series Engineering](https://mlsanity.com/paper/2508.14667/) (2025)
- [Curvature-based Feature Selection with Application in Classifying Electronic Health Records](https://mlsanity.com/paper/2101.03581/) (2021)
- [AutoFITS: Automatic Feature Engineering for Irregular Time Series](https://mlsanity.com/paper/2112.14806/) (2021)
- [Intrinsic Anomaly Detection for Multi-Variate Time Series](https://mlsanity.com/paper/2206.14342/) (2022)
- [Improving Accuracy and Explainability of Online Handwriting Recognition](https://mlsanity.com/paper/2209.09102/) (2022)
- [MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection](https://mlsanity.com/paper/2510.26643/) (2025)
- [Automated Machine Learning for Remaining Useful Life Predictions](https://mlsanity.com/paper/2306.12215/) (2023)
- [GAME: Generalized deep learning model towards multimodal data integration for early screening of adolescent mental disorders](https://mlsanity.com/paper/2309.10077/) (2023)
- [Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos](https://mlsanity.com/paper/2412.01056/) (2024)
- [Local Exceptionality Detection in Time Series Using Subgroup Discovery](https://mlsanity.com/paper/2108.11751/) (2021)
- [A Survey of Open Source Automation Tools for Data Science Predictions](https://mlsanity.com/paper/2208.11792/) (2022)
- [Distance Rank Score: Unsupervised filter method for feature selection on imbalanced dataset](https://mlsanity.com/paper/2305.19804/) (2023)
- [KPIs-Based Clustering and Visualization of HPC jobs: a Feature Reduction Approach](https://mlsanity.com/paper/2312.06534/) (2023)
- [A Novel Hybrid Feature Importance and Feature Interaction Detection Framework for Predictive Optimization in Industry 4.0 Applications](https://mlsanity.com/paper/2403.02368/) (2024)
- [Comparison of gait phase detection using traditional machine learning and deep learning techniques](https://mlsanity.com/paper/2403.05595/) (2024)
- [Meta-learning and Data Augmentation for Stress Testing Forecasting Models](https://mlsanity.com/paper/2406.17008/) (2024)
- [Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data](https://mlsanity.com/paper/2407.08003/) (2024)
- [Optimal starting point for time series forecasting](https://mlsanity.com/paper/2409.16843/) (2024)
- [Machine learning-based classification for Single Photon Space Debris Light Curves](https://mlsanity.com/paper/2411.18231/) (2024)
- [On Multivariate Financial Time Series Classification](https://mlsanity.com/paper/2504.17664/) (2025)
- [Statistical comparisons of time-series feature sets on classification tasks](https://mlsanity.com/paper/2608.01586/) (2026)
- [Automated Feature Engineering, AutoML, and Decision-Focused Learning for Improved Energy Consumption Forecasting](https://mlsanity.com/paper/2609.35013/) (2026)

## 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.