Randomized singular spectrum analysis for long time series

ABSTRACT

Singular spectrum analysis (SSA) is a relatively new method for time series analysis and comes as a non-parametric alternative to the classical methods. This methodology has proven to be effective in analysing non-stationary and complex time series since it is a non-parametric method and do not require the classical assumptions over the stationarity or over the normality of the residuals. Although SSA have proved to provide advantages over traditional methods, the challenges that arise when long time series are considered, make the standard SSA very demanding computationally and often not suitable. In this paper we propose the randomized SSA which is an alternative to SSA for long time series without losing the quality of the analysis. The SSA and the randomized SSA are compared in terms of quality of the model fit and forecasting, and computational time. This is done by using Monte Carlo simulations and real data about the daily prices of five of the major world commodities.

KEYWORDS:

• Singular spectrum analysis
• randomized singular value decomposition
• time series

AMS SUBJECT CLASSIFICATIONS:

• 62M10
• 62M15
• 62H25
• 91B84

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Paulo Canas Rodrigues http://orcid.org/0000-0002-1248-9910

Additional information

Funding

P.C. Rodrigues and R. Mahmoudvand acknowledge financial support by Comissão de Aperfeiçoamento de Pessoal do Nível Superior (Coordination for the Improvement of Higher Education Personnel), Brazil, grant number [88881.062137/2014-01].