Time series clustering based on prediction accuracy of global forecasting models

TL;DR

This paper introduces a novel time series clustering method based on the predictive accuracy of global forecasting models, emphasizing forecast quality as the primary clustering criterion, and demonstrates its superior performance through extensive experiments.

Contribution

The paper proposes a new clustering approach that uses forecast accuracy for grouping time series, offering an effective way to select the number of clusters and improve predictive performance.

Findings

Outperforms existing clustering techniques in effectiveness and accuracy.

Provides a mechanism for optimal cluster number selection.

Achieves excellent results on benchmark datasets.

Abstract

In this paper, a novel method to perform model-based clustering of time series is proposed. The procedure relies on two iterative steps: (i) K global forecasting models are fitted via pooling by considering the series pertaining to each cluster and (ii) each series is assigned to the group associated with the model producing the best forecasts according to a particular criterion. Unlike most techniques proposed in the literature, the method considers the predictive accuracy as the main element for constructing the clustering partition, which contains groups jointly minimizing the overall forecasting error. Thus, the approach leads to a new clustering paradigm where the quality of the clustering solution is measured in terms of its predictive capability. In addition, the procedure gives rise to an effective mechanism for selecting the number of clusters in a time series database and can be used in combination with any class of regression model. An extensive simulation study shows that our method outperforms several alternative techniques concerning both clustering effectiveness and predictive accuracy. The approach is also applied to perform clustering in several datasets used as standard benchmarks in the time series literature, obtaining great results.