Forecasting European industrial production with singular spectrum analysis

By Hossein Hassani, Saeed Heravi, Anatoly A. Zhigljavsky. 2009

281 citations recorded by the source corpus.

Subfields: Applied Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b9d31baf260231f275eccc18c4eff74aef31a474

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In Applied Mathematics

In Management Science and Operations Research

Citations in the corpus

Multivariate singular spectrum analysis for forecasting revisions to real-time data (2011, methodology): “The analysis here follows the literature on the empirical analysis of data revisions that defines vintage as relating to the stage of revision of the DMP, see, for example, Howrey (1978), Mork (1987) and, Akritidis (2003) and Richardson (2002, 2003).”
A Review of Some Modern Approaches to the Problem of Trend Extraction (2012, unknown): “in biology, material science, nuclear science, and, recently, in economics (Hassani et al, 2009a , 105; Thomakos, 2008 ).”
Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting (2011, methodology): “Golyandina et al. (2001) proposed Singular Spectrum Analysis-Linear Recurrent Formulae (SSA-LRF) model, and this model has been employed on some practical problems (Marques et al. 2005; Hassani et al. 2009 ).”
Predicting inflation dynamics with singular spectrum analysis (2013, background): “It is recommended that, in the presence of a periodic component with an integer period (e.g. if this component is a seasonal component) in the series, for a better separability of the periodic component, it is advisable to take the window length proportional to that period (Hassani et al., 2009).”
A review on singular spectrum analysis for economic and financial time series (2010, unknown): “Keywords and phrases: Singular spectrum analysis, Cointegration, Economic/Finacial Time Series, Filtering, Forecasting, Smoothing, Unit root, Causality.”
MULTIVARIATE SINGULAR SPECTRUM ANALYSIS: A GENERAL VIEW AND NEW VECTOR FORECASTING APPROACH (2013, methodology): “However, there are several recommendations for some particular types of series, like seasonal time series (see, for example,[1] and [4]).”
Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques (2015, unknown): “The w-correlation is interpreted such that if its value between two reconstructed components are close to 0, it confirms that the corresponding time series are w-orthogonal and are well separable (Hassani et al., 2009), and thus confirms the noise is indeed random even though residual randomness is ”
A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts (2015, methodology): “The two-sided KSPA test can be used to test for statistically significant differences between the two forecasts whilst the one-sided KSPA test can be exploited to provide statistical evidence for the claim based on [21].”
Forecasting global stock market implied volatility indices (2018, methodology): “SSA is regarded as a non-parametric technique for time series analysis and forecasting, which offers great success in forecasting economic and financial series (see for example, Hassani et al., 2009; Beneki et al., 2012).”