Abstract
Multivariate time series classification is a machine learning task with increasing importance due to the proliferation of information sources in different domains (economy, health, energy, crops, etc.). Univariate methods lack the ability to capture the relationships between the different variables that compose a multivariate time series and therefore cannot be directly extrapolated to multivariate environments. Despite the good performance and competitive results of the multivariate proposals published to date, they are hard to interpret due to their high complexity. In this paper, we propose a multivariate time series classification method based on an alternative representation of the time series, composed of a set of 41 descriptive time series features, in order to improve the interpretability of time series and results obtained. Our proposal uses traditional classifiers over the extracted features to look for relationships between the different variables that form a multivariate time series. We have selected four state-of-the-art algorithms as base classifiers to evaluate our method. We have tested our proposal on the complete University of East Anglia repository, obtaining highly interpretable results capable of explaining the relationships between the features that compose the time series and achieving performance results statistically indistinguishable from the best algorithms of the state-of-the-art.
Highlights
Nowadays, large amounts of data are generated
A key task in the analysis and mining of these data is multivariate time series classification (MTSC), which aims to give an accurate response to a large number of problems: e.g. from detecting when a patient is sick or has an anomaly in his heart behavior [28], or if a driver is in optimal condition to drive [26], the recognition of human activities [37], the occupation of an office room based on environmental information [12], the wind speed forecasting [35] or how to adapt energy production based on particular circumstances [24]
Local Cascade Ensemble for Multivariate data classification (LCEM) has a computational complexity OðN ÁsÁdÁDÁ 2D Á TBaseÞ, where d is the number of attributes, d0 is the number of attributes in Random Forest (RF) subset of attributes, D is the maximum depth of a tree, s is the number of samples, N is the number of trees, and TBase is the time complexity of the base classifier
Summary
Large amounts of data are generated. Everything is increasingly interconnected, more and more sensors are included in everything around us, and these monitor the behavior of any event of interest over time. Top accuracy is no longer the only objective and interpretability receives higher attention This applies to solutions for classification problems. In the field of MTSC, there are few proposals that pay attention to the interpretability of results [18]. Given the complexity of the problem, most proposals are focused on obtaining the best results in terms of accuracy. We present a new MTSC approach based on the representation of time series through a set of features and measures. This approach allows transforming the original MTSC problem into a traditional classification problem, enabling to apply the whole set of the traditional classification algorithms.
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