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

Hassani, Hossein, Ghodsi, Zara, Silva, Emmanuel Sirimal and Heravi, Saeed ORCID: https://orcid.org/0000-0002-0198-764X 2016. From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory. Digital Signal Processing 51 , pp. 101-109. 10.1016/j.dsp.2016.01.002

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Publisher:	Elsevier
ISSN:	1051-2004
Last Modified:	31 Oct 2022 10:49
URI:	https://orca.cardiff.ac.uk/id/eprint/86328

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