We use cookies on this website, you can read about them here. To use the website as intended please... ACCEPT COOKIES
UAL Research Online

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

Hassani, H. and Ghodzi, Z. and Silva, E.S. and Heravi, S. (2016) From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory. Digital Signal Processing, 51. pp. 101-109. ISSN 1051-2004

Type of Research: Article
Creators: Hassani, H. and Ghodzi, Z. and Silva, E.S. and Heravi, S.
Description:

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.

Additional Information (Publicly available):

This article is not Open Access due to copyright restrictions imposed by the publisher. Please contact ualresearchonline to request a copy for personal use.

Keywords/subjects not otherwise listed: Colonial Theory, Forecasting, Subspace Methods
Your affiliations with UAL: Colleges > London College of Fashion
Date: April 2016
Digital Object Identifier: 10.1016/j.dsp.2016.01.002
Date Deposited: 28 Apr 2016 10:55
Last Modified: 31 Mar 2020 15:55
Item ID: 9173
URI: https://ualresearchonline.arts.ac.uk/id/eprint/9173

Repository Staff Only: item control page | University Staff: Request a correction