Automatic Grouping in Singular Spectrum Analysis

Mahdi Kalantari and Hossein Hassani
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Mahdi Kalantari: Department of Statistics, Payame Noor University, Tehran 19395-4697, Iran
Hossein Hassani: Research Institute of Energy Management and Planning (RIEMP), University of Tehran, Tehran 1417466191, Iran

Forecasting, 2019, vol. 1, issue 1, 1-16

Abstract: 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.

Keywords: time series analysis; singular spectrum analysis (SSA); matrix norm; hierarchical clustering; corrected Rand index (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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