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Open AccessArticle

The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis

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Department of Statistics, Federal University of Bahia, 40170-110 Salvador, Brazil
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CAST, Faculty of Information Technology and Communication Sciences, Tampere University, FI-33014 Tampere, Finland
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Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology, P.O. Box 3619995161 Shahroud, Iran
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(1), 83; https://doi.org/10.3390/e22010083
Received: 7 November 2019 / Revised: 5 January 2020 / Accepted: 7 January 2020 / Published: 9 January 2020
(This article belongs to the Special Issue Data Science: Measuring Uncertainties)
Singular spectrum analysis (SSA) is a non-parametric method that breaks down a time series into a set of components that can be interpreted and grouped as trend, periodicity, and noise, emphasizing the separability of the underlying components and separate periodicities that occur at different time scales. The original time series can be recovered by summing all components. However, only the components associated to the signal should be considered for the reconstruction of the noise-free time series and to conduct forecasts. When the time series data has the presence of outliers, SSA and other classic parametric and non-parametric methods might result in misleading conclusions and robust methodologies should be used. In this paper we consider the use of two robust SSA algorithms for model fit and one for model forecasting. The classic SSA model, the robust SSA alternatives, and the autoregressive integrated moving average (ARIMA) model are compared in terms of computational time and accuracy for model fit and model forecast, using a simulation example and time series data from the quotas and returns of six mutual investment funds. When outliers are present in the data, the simulation study shows that the robust SSA algorithms outperform the classical ARIMA and SSA models. View Full-Text
Keywords: singular spectrum analysis; robust singular spectrum analysis; time series forecasting; mutual investment funds singular spectrum analysis; robust singular spectrum analysis; time series forecasting; mutual investment funds
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Rodrigues, P.C.; Pimentel, J.; Messala, P.; Kazemi, M. The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis. Entropy 2020, 22, 83.

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