Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series Forecasting.


  • Loc Vuminh Giadinh University
  • Dung Vuhoang National University of Singapore image/svg+xml




Forecasting, Fuzzy time series, Hedge algebras, Enrollments, Intervals, AITEX Index, fuzziness intervals, semantically quantifying mapping


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. After all, the results are not accurate 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, that 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.




How to Cite

Vuminh L, Vuhoang D. Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series Forecasting. EAI Endorsed Trans Context Aware Syst App [Internet]. 2018 Jun. 18 [cited 2024 Feb. 22];4(14):e2. Available from: https://publications.eai.eu/index.php/casa/article/view/1952