Supervised Feature Subset Selection and Feature Ranking for Multivariate Time Series without Feature Extraction
This addresses the need for efficient feature selection in time series analysis, particularly for applications with varied data types, though it appears incremental as it builds on existing similarity-based methods.
The paper tackles the problem of supervised feature selection for multivariate time series classification by introducing algorithms that directly compute similarity between time series without requiring feature extraction, enabling handling of heterogeneous and multi-modal data.
We introduce supervised feature ranking and feature subset selection algorithms for multivariate time series (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. Instead it is based on directly computing similarity between individual time series and assessing how well the resulting cluster structure matches the labels. The techniques are amenable to heterogeneous MTS data, where the time series measurements may have different sampling resolutions, and to multi-modal data.