# Highly comparative feature-based time-series classification

arXiv: https://arxiv.org/abs/1401.3531 · HTML: https://mlsanity.com/paper/1401.3531/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2014.

## Built on by (most cited first, 30 of 40)

- [It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks](https://mlsanity.com/paper/2602.12147/) (2026)
- [Out-of-Distribution Representation Learning for Time Series Classification](https://mlsanity.com/paper/2209.07027/) (2022)
- [DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization](https://mlsanity.com/paper/2308.02282/) (2023)
- [Time-Series Pattern Recognition in Smart Manufacturing Systems: A Literature Review and Ontology](https://mlsanity.com/paper/2301.12495/) (2023)
- [A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting](https://mlsanity.com/paper/2206.08998/) (2022)
- [End-to-End Signal Classification in Signed Cumulative Distribution Transform Space](https://mlsanity.com/paper/2205.00348/) (2022)
- [Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features](https://mlsanity.com/paper/2207.04307/) (2022)
- [Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms](https://mlsanity.com/paper/2310.02812/) (2023)
- [Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification](https://mlsanity.com/paper/2410.18686/) (2024)
- [Novel Features for Time Series Analysis: A Complex Networks Approach](https://mlsanity.com/paper/2110.09888/) (2021)
- [An Empirical Evaluation of Time-Series Feature Sets](https://mlsanity.com/paper/2110.10914/) (2021)
- [Enhancing Transformer Efficiency for Multivariate Time Series Classification](https://mlsanity.com/paper/2203.14472/) (2022)
- [Feature-Based Time-Series Analysis in R using the theft Package](https://mlsanity.com/paper/2208.06146/) (2022)
- [Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral Enhancement](https://mlsanity.com/paper/2301.10171/) (2023)
- [Never a Dull Moment: Distributional Properties as a Baseline for Time-Series Classification](https://mlsanity.com/paper/2303.17809/) (2023)
- [Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets](https://mlsanity.com/paper/2204.06540/) (2022)
- [AutoFITS: Automatic Feature Engineering for Irregular Time Series](https://mlsanity.com/paper/2112.14806/) (2021)
- [Surface Similarity Parameter: A New Machine Learning Loss Metric for Oscillatory Spatio-Temporal Data](https://mlsanity.com/paper/2204.06843/) (2022)
- [Knowledge Extraction with Interval Temporal Logic Decision Trees](https://mlsanity.com/paper/2305.16864/) (2023)
- [Similarity measure for sparse time course data based on Gaussian processes](https://mlsanity.com/paper/2102.12342/) (2021)
- [Fast leave-one-cluster-out cross-validation using clustered Network Information Criterion (NICc)](https://mlsanity.com/paper/2405.20400/) (2024)
- [Visualising Deep Network's Time-Series Representations](https://mlsanity.com/paper/2103.07176/) (2021)
- [Clustering of longitudinal data: A tutorial on a variety of approaches](https://mlsanity.com/paper/2111.05469/) (2021)
- [Hydroclimatic time series features at multiple time scales](https://mlsanity.com/paper/2112.01447/) (2021)
- [Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection](https://mlsanity.com/paper/2205.00525/) (2022)
- [FRANS: Automatic Feature Extraction for Time Series Forecasting](https://mlsanity.com/paper/2209.07018/) (2022)
- [Multivariate time series classification with dual attention network](https://mlsanity.com/paper/2308.13968/) (2023)
- [Improving the Accuracy of Transaction-Based Ponzi Detection on Ethereum](https://mlsanity.com/paper/2308.16391/) (2023)
- [KPIs-Based Clustering and Visualization of HPC jobs: a Feature Reduction Approach](https://mlsanity.com/paper/2312.06534/) (2023)
- [Adaptive Law-Based Transformation (ALT): A Lightweight Feature Representation for Time Series Classification](https://mlsanity.com/paper/2501.09217/) (2025)

## Abstract (arXiv)

A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation.