Explainability and importance estimate of time series classifier via embedded neural network.

ABSTRACT: Time series is common across disciplines, however the analysis of time series is not trivial due to inter- and intra-relationships between ordered data sequences. This imposes limitation upon the interpretation and importance estimate of the features within a time series. In the case of multivariate time series, these features are the individual time series and the time steps, which are intertwined. There exist many time series analyses, such as Autocorrelation and Granger Causality, which are based on statistic or econometric approaches. However analyses that can inform the importance of features within a time series are uncommon, especially with methods that utilise embedded methods of neural network (NN). We approach this problem by expanding upon our previous work, Pairwise Importance

SUBMITTER: Chan HTJ 

PROVIDER: S-EPMC12494753 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature