On the Optimal Parameters for Reconstruction and Forecasting in Singular Spectrum Analysis

Mahmoudvand, Rahim; Najari, Nader; Zokaei, Mohammad

doi:10.1080/03610918.2012.659822

Cited by 31 publications

(14 citation statements)

References 8 publications

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“…Scenarios (ii)‐(iv) are directly comparable as the dimension of LRF is 4 in all scenarios. Table presents the coefficients for the LRF for this illustration, which confirms the differences between our proposal and the proposal by Golyandina () and Mahmoudvand et al ().…”

Section: Resultssupporting

confidence: 89%

“…Now, we can use different accuracy criteria to compare the competing two models: the basic method of Equation 9; and new, more parsimonious, adjusted method given by Equation 18. It should be mentioned that the idea of using different L for reconstruction and for forecasting was described previously in Golyandina (2010) and Mahmoudvand, Najari, and Zokaei (2013). Let us investigate the difference between their approaches and our proposal.…”

Section: Resultsmentioning

confidence: 98%

“…It should be mentioned that the idea of using different L for reconstruction and for forecasting was described previously in Golyandina () and Mahmoudvand, Najari, and Zokaei (). Let us investigate the difference between their approaches and our proposal.…”

Section: Resultsmentioning

confidence: 99%

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A new parsimonious recurrent forecasting model in singular spectrum analysis

Mahmoudvand

Rodrigues

2017

Journal of Forecasting

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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(

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Section: Resultssupporting

confidence: 89%

Section: Resultsmentioning

confidence: 98%

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A new parsimonious recurrent forecasting model in singular spectrum analysis

Mahmoudvand

Rodrigues

2017

Journal of Forecasting

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“…In spite of the importance of the parameters, no theoretical solution has been proposed for this problem. However, there are worthwhile efforts and various techniques for selecting appropriate value of L (see other studies()). Considering the theoretical results on the structure of the trajectory matrix and on separability, it seems suitable to propose for L the half of the time series length.…”

Section: Multivariate Singular Spectrum Analysismentioning

confidence: 99%

Forecasting mortality rate by multivariate singular spectrum analysis

Mahmoudvand

Konstantinides

Rodrigues

2017

Appl Stoch Models Bus & Ind

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In this paper, we investigate the possibility of using multivariate singular spectrum analysis (SSA), a nonparametric technique in the field of time series analysis, for mortality forecasting. We consider a real data application with 9 European countries: Belgium, Denmark, Finland, France, Italy, Netherlands, Norway, Sweden, and Switzerland, over a period 1900 to 2009, and a simulation study based on the data set. The results show the superiority of multivariate SSA in comparison with the univariate SSA, in terms of forecasting accuracy.

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“…Recurrent Forecasting (R-SSA) [32] Recurrent forecasting within the SSA framework is considered the primary algorithm [33].…”

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confidence: 99%

Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods

Schwartz¹

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Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods by Michael D. Schwartz This dissertation documents an investigation into forecasting U.S. stock market equites via two very different time series analysis techniques: 1) autoregressive integrated moving average (ARIMA), and 2) singular spectrum analysis (SSA). Approximately 40% of the S&P 500 stocks are analyzed. Forecasts are generated for one and five days ahead using daily closing prices. Univariate and multivariate structures are applied and results are compared. One objective is to explore the hypothesis that a multivariate model produces superior performance over a univariate configuration. Another objective is to compare the forecasting performance of ARIMA to SSA, as SSA is a relatively recent development and has shown much potential. Stochastic characteristics of stock market data are analyzed and found to be definitely not Gaussian, but instead better fit to a generalized t-distribution. Probability distribution models are validated with goodness-of-fit tests. For analysis, stock data is segmented into non-overlapping time "windows" to support unconditional statistical evaluation. Univariate and multivariate ARIMA and SSA time series models are evaluated for independence. ARIMA models are found to be independent, but SSA models are not able to reach independence. Statistics for out-of-sample forecasts are computed for every stock viii in every window, and multivariate-univariate confidence interval shrinkages are examined. Results are compared for univariate, bivariate, and trivariate combinations of highlycorrelated stocks. Effects are found to be mixed. Bivariate modeling and forecasting with three different covariates are investigated. Examination of results with covariates of trading volume, principal component analysis (PCA), and volatility reveal that PCA exhibits the best overall forecasting accuracy in the entire field of investigated elements, including univariate models. Bivariate-PCA structures are applied in a back-testing environment to evaluate economic significance and robustness of the methods. Initial results of back-testing yielded similar results to those from earlier independent testing. Inconsistent performance across test intervals inspired the development of a second technique that yields improved results and positive economic significance. Robustness is validated through back-testing across multiple market trends.

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On the Optimal Parameters for Reconstruction and Forecasting in Singular Spectrum Analysis

Cited by 31 publications

References 8 publications

A new parsimonious recurrent forecasting model in singular spectrum analysis

A new parsimonious recurrent forecasting model in singular spectrum analysis

Forecasting mortality rate by multivariate singular spectrum analysis

Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods

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