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

Hossein Hassani*, Zara Ghodsi, Emmanuel Sirimal Silva, Saeed Heravi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)101-109
Number of pages9
JournalDigital Signal Processing: A Review Journal
Volume51
Early online date17 Feb 2016
DOIs
Publication statusPublished - Apr 2016
Externally publishedYes

Keywords

  • Colonial theory
  • Forecasting
  • Nature inspired algorithm
  • Subspace methods

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics
  • Electrical and Electronic Engineering

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