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Time Series Forecasting Using Independent Component Analysis
Authors: Theodor D. Popescu
Abstract:The paper presents a method for multivariate time series forecasting using Independent Component Analysis (ICA), as a preprocessing tool. The idea of this approach is to do the forecasting in the space of independent components (sources), and then to transform back the results to the original time series space. The forecasting can be done separately and with a different method for each component, depending on its time structure. The paper gives also a review of the main algorithms for independent component analysis in the case of instantaneous mixture models, using second and high-order statistics. The method has been applied in simulation to an artificial multivariate time series with five components, generated from three sources and a mixing matrix, randomly generated.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074779Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1445
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