Key result
A new dissimilarity measure based on matrix norms combined with hierarchical clustering linkages was proposed to optimize the time-consuming grouping step in singular spectrum analysis.
Why the study?
The grouping step of singular spectrum analysis is time consuming because interpretable components are manually selected, motivating automatic grouping approaches.
This methodological paper proposes a new automatic grouping approach for singular spectrum analysis to optimize the time-consuming manual selection process.
May reduce manual effort in SSA grouping for forecasting; leaves open validation in cardiovascular time series.
Singular spectrum analysis (SSA) is a non-parametric forecasting and filtering method that has many applications in a variety of fields such as signal processing, economics and time series analysis. One of the four steps of the SSA, which is called the grouping step, plays a pivotal role in the SSA because reconstruction and forecasting of results are directly affected by the outputs of this step. Usually, the grouping step of SSA is time consuming as the interpretable components are manually selected. An alternative more optimized approach is to apply automatic grouping methods. In this paper, a new dissimilarity measure between two components of a time series that is based on various matrix norms is first proposed. Then, using the new dissimilarity matrices, the capabilities of different hierarchical clustering linkages are compared to identify appropriate groups in the SSA grouping step. The performance of the proposed approach is assessed using the corrected Rand index as validation criterion and utilizing various real-world and simulated time series.
No takes yet. Share an insight, caveat, or question.
Kalantari et al. (2019) studied Time series analysis. Automatic grouping methods based on matrix norms and hierarchical clustering vs. Manual selection of interpretable components was evaluated on Performance assessed using the corrected Rand index. A new dissimilarity measure based on matrix norms combined with hierarchical clustering linkages was proposed to optimize the time-consuming grouping step in singular spectrum analysis.