Symbolic representation of time series: A hierarchical coclustering formalization

Abstract

The choice of an appropriate representation remains crucial for mining time series, particularly to reach a good trade-off between the dimensionality reduction and the stored information. Symbolic representations constitute a simple way of reducing the dimensionality by turning time series into sequences of symbols. SAXO is a data-driven symbolic representation of time series which encodes typical distributions of data points. This approach was first introduced as a heuristic algorithm based on a regularized coclustering approach. The main contribution of this article is to formalize SAXO as a hierarchical coclustering approach. The search for the best symbolic representation given the data is turned into a model selection problem. Comparative experiments demonstrate the benefit of the new formalization, which results in representations that drastically improve the compression of data while keeping useful information for classification tasks.