From Nature to Maths: Improving Forecasting Performance in Subspace–based methods using Genetics Colonial Theory.

Hassani, H., Ghodsi, Z., Silva, E. and Heravi, S., 2016. From Nature to Maths: Improving Forecasting Performance in Subspace–based methods using Genetics Colonial Theory. Digital Signal Processing, 51 (April), pp. 101-109.

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DOI: 10.1016/j.dsp.2016.01.002

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
ISSN:1051-2004
Uncontrolled Keywords:Colonial theory; Forecasting; Nature inspired algorithm; Subspace methods
Group:Faculty of Management
ID Code:29722
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:19 Sep 2017 14:48
Last Modified:19 Sep 2017 14:48

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