Highly comparative feature-based time-series classification (1401.3531v2)

Abstract: 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.

• The paper introduces a method that extracts thousands of features to convert raw time series into compact, interpretable representations.
• The paper applies greedy forward feature selection with a linear classifier, matching or surpassing traditional techniques like DTW.
• The paper demonstrates efficiency by reducing high-dimensional data to an average of 3 key features per dataset for scalable analysis.

Time-Series Classification through Highly Comparative Feature-Based Approaches

The paper "Highly Comparative Feature-Based Time-Series Classification" by Ben D. Fulcher and Nick S. Jones presents an innovative method for classifying time-series data using a large database of feature extraction techniques derived from broad scientific literature. The approach critiques traditional methods that primarily rely on sequential comparisons between time-series data and introduces a feature-based framework that encapsulates the dynamic properties of time series through a multitude of descriptive properties.

Methodology and Approach

The proposed method leverages a substantial library of over 9,000 time-series analysis features to extract meaningful, interpretable characteristics from a given dataset. These features are sourced from various domains, including statistical summaries, spectral analysis, entropy assessments, and non-linear dynamic analysis. The core advantage of this approach is its ability to convert a time series into a compact set of features that captures its intrinsic properties, allowing for significant reductions in data dimensionality.

The method applies greedy forward feature selection techniques to identify the most informative features that contribute to class discrimination. The use of a linear classifier in combination with forward feature selection optimizes the classification task and avoids excessive reliance on computationally expensive distance metrics typically used in instance-based classification. By selecting and interpreting features directly, the classifier can provide insight into underlying data characteristics that define class boundaries.

Performance Evaluation

The paper assesses the efficacy of this feature-based framework across twenty different datasets from the UCR Time Series Classification/Clustering archive. The results demonstrate the competitiveness of the approach: feature-based classifiers match or exceed the performance of traditional methods such as dynamic time warping (DTW) and Euclidean distance-based nearest neighbor classifiers on numerous datasets. Importantly, the feature-based method achieves substantial dimensionality reduction and offers interpretability in understanding the key discriminative properties of time-series data.

Across these datasets, the average number of features used per dataset was approximately 3.2, in contrast to the average full length of the time series, which was 282.1 samples. This level of reduction not only highlights the efficiency of the method but also underscores its potential utility in large-scale data applications where computational resources are a bottleneck.

Implications and Future Directions

The implications of this research are profound for fields requiring rapid classification at scale and where interpretability of the results is crucial. The feature-based framework can be pivotal in diverse areas such as anomaly detection in industrial processes, dynamic signal analysis in medicine, and time-series prediction in finance.

Future work can explore the integration of more sophisticated classifiers and feature selection techniques to enhance performance further. Additionally, there is scope for expanding the database of features through interdisciplinary collaboration, potentially incorporating new forms of analysis developed in emerging scientific areas. The adaptability of this method to time series of varying length and type signifies its potential as a generic, robust tool for time-series analysis across a spectrum of applications.

In conclusion, the paper presents a compelling case for rethinking traditional time-series classification methods. By framing the classification task within a rich feature-based context, this paper paves the way for more efficient, interpretable, and scalable solutions in the rapidly expanding field of temporal data mining.