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A new parsimonious recurrent forecasting model in singular spectrum analysis

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  • Rahim Mahmoudvand
  • Paulo Canas Rodrigues

Abstract

Singular spectrum analysis (SSA) is a powerful nonparametric method in the area of time series analysis that has shown its capability in different applications areas. SSA depends on two main choices: the window length L and the number of eigentriples used for grouping r. One of the most important issues when analyzing time series is the forecast of new observations. When using SSA for time series forecasting there are several alternative algorithms, the most widely used being the recurrent forecasting model, which assumes that a given observation can be written as a linear combination of the L−1 previous observations. However, when the window length L is large, the forecasting model is unlikely to be parsimonious. In this paper we propose a new parsimonious recurrent forecasting model that uses an optimal m(

Suggested Citation

  • Rahim Mahmoudvand & Paulo Canas Rodrigues, 2018. "A new parsimonious recurrent forecasting model in singular spectrum analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(2), pages 191-200, March.
    • Handle: RePEc:wly:jforec:v:37:y:2018:i:2:p:191-200
    DOI: 10.1002/for.2484
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    Cited by:

    1. Miguel de Carvalho & Gabriel Martos, 2022. "Modeling interval trendlines: Symbolic singular spectrum analysis for interval time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 167-180, January.
    2. Sulandari, Winita & Subanar, & Lee, Muhammad Hisyam & Rodrigues, Paulo Canas, 2020. "Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks," Energy, Elsevier, vol. 190(C).
    3. Paulo Canas Rodrigues & Olushina Olawale Awe & Jonatha Sousa Pimentel & Rahim Mahmoudvand, 2020. "Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks," Stats, MDPI, vol. 3(2), pages 1-21, June.

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