A review on singular spectrum analysis for economic and financial time series

Hassani, Hossein; Thomakos, Dimitrios D.

doi:10.4310/sii.2010.v3.n3.a11

Cited by 150 publications

(81 citation statements)

References 49 publications

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“…The SSA method is made up of two complementary stages: Decomposition and Reconstruction; each stage consists of two compatible steps. At the first stage a group of small number of independent and interpretable components is achieved by decomposing the main series [23], which is followed by the reconstruction of a less noisy series at the second stage [24]. Thereafter, this noise free series is used for forecasting future data points.…”

Section: Similarities Between Ssa and Ctmentioning

confidence: 99%

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Hassani

Ghodsi

Silva

et al. 2016

Digital Signal Processing

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Many scientific fields consider accurate and reliable forecasting methods as important decision-making tools in the modern age amidst increasing volatility and uncertainty. As such there exists an opportune demand for theoretical developments which can result in more accurate forecasts. Inspired by Colonial Theory, this paper seeks to bring about considerable improvements to the field of time series analysis and forecasting by identifying certain core characteristics of Colonial Theory which are subsequently exploited in introducing a novel approach for the grouping step of subspace based methods. The proposed algorithm shows promising results in terms of improved performances in noise filtering and forecasting of time series. The reliability and validity of the proposed algorithm is evaluated and compared with popular forecasting models with the results being thoroughly evaluated for statistical significance and thereby adding more confidence and value to the findings of this research.

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Section: Similarities Between Ssa and Ctmentioning

confidence: 99%

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Hassani

Ghodsi

Silva

et al. 2016

Digital Signal Processing

Self Cite

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“…After Broomhead and King (1986) theoretically proposed the MSSA technique in the context of nonlinear dynamics for the first time, it has been widely applied on a range of different fields and a multitude of fairly precise results proved it as powerful and applicable technique, numerous applications and examples can be found in (Hassani, 2007;Hassani et al, 2009Hassani et al, , 2013aHassani et al, , 2013bGhodsi et al, 2010;Hassani and Thomakos, 2010;Hassani and Mahmoudvand, 2013). From the perspective of MSSA, two main concerns that make the problem more complex are: i) similarity and orthogonality among series play an important rule for selecting the window length L and the number of eigenvalues r, and ii) MSSA deals with a block trajectory Hankel matrix with special features rather than one simple Hankel matrix (Hassani and Mahmoudvand, 2013).…”

Section: Methodology: the Ssa-based Causality Test (Mssa)mentioning

confidence: 99%

“…Given the importance of the issue of global warming, and more importantly the lack of evidence in favor of sunspot numbers leading to global temperatures in linear models, our current paper aims to revisit this issue of whether sunspot numbers cause global temperatures, using the same data set and sub-samples used by Gupta et al, (2015), based on Singular Spectrum Analysis (SSA) technique, which is a new nonparametric technique known for both time series analysis and forecasting (as discussed further in Hassani, 2007;Hassani and Thomakos, 2010;Hassani et al, 2009Hassani et al, , 2013aHassani et al, , 2013bHassani and Mahmoudvand, 2013). The reason behind using a nonparametric technique is to capture possible nonlinearities that could exist in the data generating processes of the global temperatures and sunspots, as well as, in the relationship between global temperatures and sunspot activity, for instance due to the structural breaks detected by Gupta et al, (2015).…”

Section: Introductionmentioning

confidence: 99%

Does sunspot numbers cause global temperatures? A reconsideration using non-parametric causality tests

Hassani¹,

Huang

Gupta

et al. 2016

Physica A: Statistical Mechanics and its Applications

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Highlights• A non-parametric Singular Spectrum Analysis based causality test is proposed.• SSA-based causality test outperformed time and frequency domain causality tests.• The new non-parametric technique can capture the possibly existing nonlinearities.• Predictive ability is detected from sunspot numbers on global temperatures. ABSTRACTIn a recent paper, Gupta et al., (2015), analyzed whether sunspot numbers cause global temperatures based on monthly data covering the period 1880:1-2013:9. The authors find that standard time domain Granger causality test fails to reject the null hypothesis that sunspot numbers does not cause global temperatures for both full and sub-samples, namely 1880: 1-1936:2, 1936:3-1986:11 and 1986:12-2013:9 (identified based on tests of structural breaks). However, frequency domain causality test detects predictability for the full-sample at short (2 to 2.6 months) cycle lengths, but not the sub-samples. But since, full-sample causality cannot be relied upon due to structural breaks, Gupta et al., (2015) concludes that the evidence of causality running from sunspot numbers to global temperatures is weak and inconclusive. Given the importance of the issue of global warming, our current paper aims to revisit this issue of whether sunspot numbers cause global temperatures, using the same data set and sub-samples used by Gupta et al., (2015), based on an nonparametric Singular Spectrum Analysis (SSA)-based causality test. Based on this test, we however, show that sunspot numbers have predictive ability for global temperatures for the three sub-samples, over and above the full-sample. Thus, generally speaking, our non-parametric SSA-based causality test outperformed both time domain and frequency domain causality tests and highlighted that sunspot numbers have always been important in predicting global temperatures.Keywords: Causality; Singular Spectrum Analysis; frequency domain; global temperatures predictability; sunspot numbers JEL classification: C32 1 IntroductionGlobal warming, i.e., rising temperature of the earth's surface, is undoubtedly the biggest topic of research amongst researchers working on environment. While, analyzing the impact of global warming cannot be ignored, but what factors drive it is perhaps more important, as it not only allows us to predict global warming, but also takes measures to control it. It is quite well-accepted that global warming is due to greenhouse gases, additionally, there is a large literature 1 that relates the same with solar activity. However, the evidence from this literature is, at best, mixed. While there are studies (see for example, Rind, 1998, 2009; West, 2003, 2005;Scafetta et al., 2004;Scaffeta, 2009 Scaffeta, , 2011Folland et al., 2013;Zhou and Tung, 2013) that find significant relationships between solar radiation and global temperatures, one one hand. On the other hand, there are some authors who claim that the two variables are unrelated (see for example, Pittock, 1978Pittock, , 1983Pittock, , 2009Love et al., 2011;Usoskin, e...

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“…SSA has also been applied to the area of finance and economics. Hassani and Thomakos [15] reviewed recent developments in the theoretical and methodological aspects of SSA in the area of economic and financial time series, and also present some new results. Again, Hassani et al [16] applied univariate and multivariate singular spectrum analysis for predicting the value of changes in the daily pound/dollar exchange rate.…”

Section: On Developments and Applications Of Singular Spectrum Analysismentioning

confidence: 99%

Modeling the Trend of Credit Card Usage Behavior for Different Age Groups Based on Singular Spectrum Analysis

Nai¹,

Liu

Wang³

et al. 2018

Algorithms

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Credit card holders from different age groups have different usage behaviors, so deeply investigating the credit card usage condition and properly modeling the usage trend of all customers in different age groups from time series data is meaningful for financial institutions as well as banks. Until now, related research in trend analysis of credit card usage has mostly been focused on specific group of people, such as the behavioral tendencies of the elderly or college students, or certain behaviors, such as the increasing number of cards owned and the rise in personal card debt or bankruptcy, in which the only analysis methods employed are simply enumerating or classifying raw data; thus, there is a lack of support in specific mathematical models based on usage behavioral time series data. Considering that few systematic modeling methods have been introduced, in this paper, a novel usage trend analysis method for credit card holders in different age groups based on singular spectrum analysis (SSA) has been proposed, using the time series data from the Survey of Consumer Payment Choice (SCPC). The decomposition and reconstruction process in the method is proposed. The results show that the credit card usage frequency falls down from the age of 26 to the lowest point at around the age of 58 and then begins to increase again. At last, future work is discussed.

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A review on singular spectrum analysis for economic and financial time series

Cited by 150 publications

References 49 publications

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Does sunspot numbers cause global temperatures? A reconsideration using non-parametric causality tests

Modeling the Trend of Credit Card Usage Behavior for Different Age Groups Based on Singular Spectrum Analysis

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