Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series Forecasting
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-semanti...
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Online Access: | http://eudl.eu/doi/10.4108/eai.18-6-2018.154820 |
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doaj-b3d517a7ac8a4427ba71f22a4c6ea6da2020-11-25T02:36:32ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Context-aware Systems and Applications2409-00262018-06-0141411110.4108/eai.18-6-2018.154820Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series ForecastingLoc Vuminh0Dung Vuhoang1Giadinh University, Vungtau City, Vietnam; vuminhloc@gmail.comNational University Of Singapore, SingaporeDuring 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.http://eudl.eu/doi/10.4108/eai.18-6-2018.154820ForecastingFuzzy time seriesHedge algebrasEnrollmentsIntervalsAITEX Indexfuzziness intervalssemantically quantifying mapping |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Loc Vuminh Dung Vuhoang |
spellingShingle |
Loc Vuminh Dung Vuhoang Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series Forecasting EAI Endorsed Transactions on Context-aware Systems and Applications Forecasting Fuzzy time series Hedge algebras Enrollments Intervals AITEX Index fuzziness intervals semantically quantifying mapping |
author_facet |
Loc Vuminh Dung Vuhoang |
author_sort |
Loc Vuminh |
title |
Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series Forecasting |
title_short |
Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series Forecasting |
title_full |
Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series Forecasting |
title_fullStr |
Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series Forecasting |
title_full_unstemmed |
Hedge Algebra Approach for Fuzzy Time series To Improve Result Of Time Series Forecasting |
title_sort |
hedge algebra approach for fuzzy time series to improve result of time series forecasting |
publisher |
European Alliance for Innovation (EAI) |
series |
EAI Endorsed Transactions on Context-aware Systems and Applications |
issn |
2409-0026 |
publishDate |
2018-06-01 |
description |
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. |
topic |
Forecasting Fuzzy time series Hedge algebras Enrollments Intervals AITEX Index fuzziness intervals semantically quantifying mapping |
url |
http://eudl.eu/doi/10.4108/eai.18-6-2018.154820 |
work_keys_str_mv |
AT locvuminh hedgealgebraapproachforfuzzytimeseriestoimproveresultoftimeseriesforecasting AT dungvuhoang hedgealgebraapproachforfuzzytimeseriestoimproveresultoftimeseriesforecasting |
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