Unsupervised feature selection for sensor time-series in pervasive computing applications
- Davide Bacciu
- Neural Computing and Applications
The main objective of ‘‘time series analysis’’ is to discover the underlying structure of the time series, and thus, become able to forecast its ‘‘future values’’. This process makes it possible to predict, control or simulate variables. Most of the time series modelling procedures try to forecast future values from lagged ones. Thus, the selection of the relevant lagged values to be used is a key step. In this paper, a new consensus method for the selection of relevant lagged values of a time series is introduced: feature ranking aggregated selection (FRASel). The main contribution of this feature selection method is the definition of a consensus decision making mechanism based on aggregation and expressed as a simple rule. In FRASel, the selected subset of lagged values is decided by the application of an aggregation criterion to the results of different flavours of feature ranking methods, applied from different approaches. A thorough empirical analysis is carried out to assess the performance of FRASel. The statistical significance of the experimental results is also analysed through the application of non-parametric statistical tests.