Randomized singular spectrum analysis for long time series
| Autor: | Paulo Canas Rodrigues, Rahim Mahmoudvand, Pétala Gardênia da Silva Estrela Tuy |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
010504 meteorology & atmospheric sciences Series (mathematics) Applied Mathematics media_common.quotation_subject Monte Carlo method 010502 geochemistry & geophysics 01 natural sciences Modeling and Simulation Applied mathematics Quality (business) Statistics Probability and Uncertainty Time series Spectrum analysis Singular spectrum analysis Normality 0105 earth and related environmental sciences Mathematics media_common |
| Zdroj: | Journal of Statistical Computation and Simulation. 88:1921-1935 |
| ISSN: | 1563-5163 0094-9655 |
| DOI: | 10.1080/00949655.2018.1462810 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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