Hedge Algebra Approach for Semantics-Based Algorithm to Improve Result of Time Series Forecasting

Abstract

During the recent years, many different methods of using fuzzy time series for forecasting have been published. However, computation in the linguistic environment one term has two parallel semantics, one represented by fuzzy sets (computation-semantics) it human-imposed and the rest (context-semantic) is due to the context of the problem. If the latter semantics is not paid attention, despite the computation accomplished high level of exactly but it has been distorted about semantics. That means the result does not suitable the context of the problem. Hedge Algebras, an algebraic Approach to domains of linguistic variables, unifying the above two semantics of each term, is the basis of convenient calculation in the language environment and does not distort the semantics of terms. A new approach is proposed through a semantic-based algorithm, focus on two key steps: partitioning the universe of discourse of time series into a collection of intervals and mining fuzzy relationships from fuzzy time series, which outperforms accuracy and friendliness in computing.

The experimental results, forecasting enrollments at the University of Alabama and forecasting TAIEX Index, demonstrate that the proposed method significantly outperforms the published ones about accurate level, the ease and friendliness on computing.