casa 18(14): e2

Research Article

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

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  • @ARTICLE{10.4108/eai.18-6-2018.154820,
        author={Loc  Vuminh and Dung Vuhoang},
        title={Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series Forecasting.},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
        volume={4},
        number={14},
        publisher={EAI},
        journal_a={CASA},
        year={2018},
        month={6},
        keywords={Forecasting; Fuzzy time series; Hedge algebras; Enrollments, Intervals;AITEX Index; fuzziness intervals; semantically quantifying mapping.},
        doi={10.4108/eai.18-6-2018.154820}
    }
    
  • Loc Vuminh
    Dung Vuhoang
    Year: 2018
    Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series Forecasting.
    CASA
    EAI
    DOI: 10.4108/eai.18-6-2018.154820
Loc Vuminh1,*, Dung Vuhoang2
  • 1: Giadinh University, Vungtau City, Vietnam
  • 2: National University Of Singapore, Singapore
*Contact email: vuminhloc@gmail.com

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. 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.