Forecasting energy data using Singular Spectrum Analysis in the presence of outlier(s)

Ghodsi, Zara; Omer, Hardi Nabe

doi:10.1142/s2335680414500094

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…There are several recent and diverse applications which have benefited from these qualities. See for example, inter-alia Huang et al (2017), Ghodsi (2015), Ghodsi et al (2015), Ghodsi and Omer (2014) and Cassiano et al (2013). MSSA in particular offers the ability of modelling time series with different series lengths, see for example .…”

Section: Define Vector Zmentioning

confidence: 99%

Cross country relations in European tourist arrivals

Silva¹,

Ghodsi²,

Ghodsi³

et al. 2017

Annals of Tourism Research

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This paper introduces an optimized Multivariate Singular Spectrum Analysis (MSSA) algorithm for identifying leading indicators. Exploiting European tourist arrivals data, we analyse cross country relations for European tourism demand. Cross country relations have the potential to aid in planning and resource allocations for future tourism demand by taking into consideration the variation in tourist arrivals across other countries in Europe. Our findings indicate with statistically significant evidence that there exists cross country relations between European tourist arrivals which can help in improving the predictive accuracy of tourism demand. We also find that MSSA has the capability of not only identifying leading indicators, but also forecasting tourism demand with far better accuracy in comparison to its univariate counterpart, Singular Spectrum Analysis.

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Section: Define Vector Zmentioning

confidence: 99%

Cross country relations in European tourist arrivals

Silva¹,

Ghodsi²,

Ghodsi³

et al. 2017

Annals of Tourism Research

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“…Some studies have employed the SSA method to forecast financial time series (Hassani et al, 2013b;Ghodsi and Omer, 2014). The SSA method comprises two stages, one is decomposition and the other is reconstruction.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…One widely used technique is singular spectrum analysis (SSA), which is a robust nonparametric method with no prior assumptions about the data (Golyandina et al, 2001;Hassani et al, 2013a). SSA decomposes a time series data into its components and then reconstructs the series by leaving the random noise component before using the reconstructed series to forecast the future points in the series (Hassani, 2007;Ghodsi and Omer, 2014). Since most financial time series data sets exhibit neither purely linear nor purely nonlinear patterns, the combination of linear and nonlinear, i.e., hybrid techniques to model complex data structures for improved accuracy has been proposed (Asadi et al, 2010;Khashei and Bijari, 2010;Khashei and Bijari, 2012;Khandelwal et al, 2015;Ince and Trafalis, 2017).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of forecasting methods from selected stock market returns

Mallikarjuna

Rao

2019

Financ Innov

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Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification. There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making. Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices. However, there have been very few studies of groups of stock markets or indices. The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets. In this context, this study aimed to examine the predictive performance of linear, nonlinear, artificial intelligence, frequency domain, and hybrid models to find an appropriate model to forecast the stock returns of developed, emerging, and frontier markets. We considered the daily stock market returns of selected indices from developed, emerging, and frontier markets for the period 2000-2018 to evaluate the predictive performance of the above models. The results showed that no single model out of the five models could be applied uniformly to all markets. However, traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts.

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