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 language | English |
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Pages (from-to) | 101-109 |
Number of pages | 9 |
Journal | Digital Signal Processing: A Review Journal |
Volume | 51 |
Early online date | 17 Feb 2016 |
DOIs | |
Publication status | Published - Apr 2016 |
Externally published | Yes |
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