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

Abstract

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.

Time series forecasting using singular spectrum analysis, fuzzy systems and neural networks

“…Related literature can be found in [29] , [30] , [31] . In the case of data contamination with outlying observations, further improvement related to the SSA part of the model can be obtained by considering a robust SSA algorithm [25 , 32] . A more parsimonious adaptation of the recurrent forecast algorithm [33] or of the vector forecast algorithm [34] can also be considered to improve the forecasting ability of the SSA part of the model.…”

A Data-Characteristic-Driven Decomposition Ensemble Forecasting Research on the Demand of Space Science Payload Components

“…The model based on decomposition shows better performance than the traditional single model in the prediction of unsteady and nonlinear data. Time series data decomposition method includes wavelet transform (WT) [11], Fourier transform (FT) [12], empirical mode decomposition (EMD) [13], and singular spectrum analysis (SSA) [14]. EMD is superior to other decomposition methods because it is very suitable for complex unsteady and nonlinear time series and easy to model, but it also has the problem of modal aliasing [15].…”

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

“…SSA is an advanced time series analysis tool, which is classified as a nonparametric spectral estimation technique [ 32 ]. It has been used in forecasting different engineering time series such as annual precipitation and hourly water temperature [ 33 ], mutual investment funds [ 34 ], wind speed [ 35 ] and electricity consumption [ 36 ]. In a typical SSA model, a conventional time series analysis is integrated with signal processing, multivariate geometry, multivariate statistics and dynamical systems.…”