Statistical comparisons of time-series feature sets on classification tasks

• URL: http://arxiv.org/abs/2608.01586v1
• Date: Mon, 03 Aug 2026 01:43:50 GMT
• Title: Statistical comparisons of time-series feature sets on classification tasks
• Abstract summary: We benchmarked six open-source feature sets and three baseline feature sets on time-series classification problems.<n>Despite dramatic differences in size, composition, and time, feature sets performed relatively similarly overall.<n>We highlighted specific problems on which the specific composition of a given feature set gave it a substantial performance advantage or disadvantage.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, numerous open-source software libraries have been developed for computing sets of features from univariate time series. The type and number of features vary across these feature sets, which have been constructed with varying disciplinary perspectives on quantifying structure in time-series data. To date, the relative strengths and weaknesses of these feature sets on time-series classification problems remains largely unexplored. Here we aimed to understand the relative performance of six open-source feature sets and three baseline feature sets (based on distributional and/or basic spectral structure) across 124 univariate time-series classification problems using a normalization-based approach to problem-level benchmarking that better indexes the relative strengths and weaknesses of different algorithms compared to prior rank-based approaches. Despite their dramatic differences in size, composition, and computation time, we found that feature sets performed relatively similarly overall (85.3% of pairwise comparisons resulted in ties), with the largest feature set, tsfresh, exhibiting the strongest overall performance (29.03% wins across all pairwise comparisons against other feature sets). We also highlighted specific problems on which the specific composition of a given feature set gave it a substantial performance advantage or disadvantage, and problems where simple baselines comprised of Fourier coefficients and quantiles were sufficient to achieve strong performance. Our results demonstrate the need to consider problem-level performance when benchmarking time-series feature sets, and highlight the importance of feature make-up in driving relative classification performance.

Related papers

• FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification [56.925103708982164]
  We present a novel perspective from the frequency domain and identify three advantages for downstream classification: global, independent, and compact.<n>We propose the lightweight yet effective Frequency Refined Augmentation (FreRA) tailored for time series contrastive learning on classification tasks.<n>FreRA consistently outperforms ten leading baselines on time series classification, anomaly detection, and transfer learning tasks.
  arXiv  Detail & Related papers  (2025-05-29T07:18:28Z)
• Capturing Temporal Components for Time Series Classification [5.70772577110828]
  This work introduces a textitcompositional representation learning approach trained on statistically coherent components extracted from sequential data. Based on a multi-scale change space, an unsupervised approach is proposed to segment the sequential data into chunks with similar statistical properties. A sequence-based encoder model is trained in a multi-task setting to learn compositional representations from these temporal components for time series classification.
  arXiv  Detail & Related papers  (2024-06-20T16:15:21Z)
• Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models [61.10851158749843]
  Key insights can be obtained by discovering lead-lag relationships inherent in the data. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models.
  arXiv  Detail & Related papers  (2023-05-11T10:30:35Z)
• Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification [0.0]
  We evaluate the performance of an extremely simple classification approach. We find that a simple linear model based on the mean and standard deviation performs better at classifying individuals with schizophrenia.
  arXiv  Detail & Related papers  (2023-03-31T05:55:54Z)
• Feature-Based Time-Series Analysis in R using the theft Package [0.0]
  Many open-source software packages for computing sets of time-series features exist across multiple programming languages. Here we introduce a solution to these issues in an R software package called theft.
  arXiv  Detail & Related papers  (2022-08-12T07:29:29Z)
• Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing [88.76112371510999]
  Federated learning is a prime candidate for distributed machine learning at the network edge. Existing algorithms face issues with slow convergence and/or robustness of performance. We propose a contextual aggregation scheme that achieves the optimal context-dependent bound on loss reduction.
  arXiv  Detail & Related papers  (2022-03-23T21:42:31Z)
• Cluster-and-Conquer: A Framework For Time-Series Forecasting [94.63501563413725]
  We propose a three-stage framework for forecasting high-dimensional time-series data. Our framework is highly general, allowing for any time-series forecasting and clustering method to be used in each step. When instantiated with simple linear autoregressive models, we are able to achieve state-of-the-art results on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-26T20:41:19Z)
• An Empirical Evaluation of Time-Series Feature Sets [0.0]
  Seven different feature sets can be used to solve time-series problems with features. Here we compare these sets on computational speed, assess the redundancy of features contained in each, and evaluate the overlap and redundancy between them. We find that feature sets vary across three orders of magnitude in their computation time per feature on a laptop for a 1000-sample series.
  arXiv  Detail & Related papers  (2021-10-21T06:06:46Z)
• Novel Features for Time Series Analysis: A Complex Networks Approach [62.997667081978825]
  Time series data are ubiquitous in several domains as climate, economics and health care. Recent conceptual approach relies on time series mapping to complex networks. Network analysis can be used to characterize different types of time series.
  arXiv  Detail & Related papers  (2021-10-11T13:46:28Z)
• Supervised Feature Subset Selection and Feature Ranking for Multivariate Time Series without Feature Extraction [78.84356269545157]
  We introduce supervised feature ranking and feature subset selection algorithms for MTS classification. Unlike most existing supervised/unsupervised feature selection algorithms for MTS our techniques do not require a feature extraction step to generate a one-dimensional feature vector from the time series.
  arXiv  Detail & Related papers  (2020-05-01T07:46:29Z)
• Complexity Measures and Features for Times Series classification [0.0]
  We propose a set of characteristics capable of extracting information on the structure of the time series to face time series classification problems. The experimental results of our proposal show no statistically significant differences from the second and third best models of the state-of-the-art.
  arXiv  Detail & Related papers  (2020-02-27T11:08:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.