A Multivariate Fuzzy Hidden Markov Model For Time Series Forecasting
碩士 === 明新科技大學 === 管理研究所在職專班 === 102 === Forecasting is the scientific technology to predict the future events. An appropriate forecasting model can not only acquire precise results, but also provide the foundation of decision making. After reviewing some relative researches on fuzzy time series, mos...
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ndltd-TW-102MHIT11880022015-10-13T22:57:21Z http://ndltd.ncl.edu.tw/handle/66819458822895181177 A Multivariate Fuzzy Hidden Markov Model For Time Series Forecasting 模糊多因子隱藏式馬可夫模型於時間序列之預測應用 Lu, Jen-Ho 呂仁和 碩士 明新科技大學 管理研究所在職專班 102 Forecasting is the scientific technology to predict the future events. An appropriate forecasting model can not only acquire precise results, but also provide the foundation of decision making. After reviewing some relative researches on fuzzy time series, most of the forecasting model is suitable for only one-factor or two-factor problems. However, there isn’t only one factor that affect the result, therefore, it is important to develop a forecasting model which can deal with more factors. For this reason, we present a multivariate HMM-based forecasting model to offer the demand of more than three factors. Based on the multivariate model, experiments of the daily average temperature in Taipei from 1993 to 1996 and Taiwan Weighted Stock Index from 2005 to 2007 could be developed. The experiments proved the hypothesis that multivariate model has lower inaccuracy than tranditional fuzzy time series models in relative researches. Hsien-Lun Wong 王賢崙 2014 學位論文 ; thesis 68 zh-TW |
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碩士 === 明新科技大學 === 管理研究所在職專班 === 102 === Forecasting is the scientific technology to predict the future events. An appropriate forecasting model can not only acquire precise results, but also provide the foundation of decision making. After reviewing some relative researches on fuzzy time series, most of the forecasting model is suitable for only one-factor or two-factor problems. However, there isn’t only one factor that affect the result, therefore, it is important to develop a forecasting model which can deal with more factors. For this reason, we present a multivariate HMM-based forecasting model to offer the demand of more than three factors. Based on the multivariate model, experiments of the daily average temperature in Taipei from 1993 to 1996 and Taiwan Weighted Stock Index from 2005 to 2007 could be developed. The experiments proved the hypothesis that multivariate model has lower inaccuracy than tranditional fuzzy time series models in relative researches.
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Hsien-Lun Wong |
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Hsien-Lun Wong Lu, Jen-Ho 呂仁和 |
author |
Lu, Jen-Ho 呂仁和 |
spellingShingle |
Lu, Jen-Ho 呂仁和 A Multivariate Fuzzy Hidden Markov Model For Time Series Forecasting |
author_sort |
Lu, Jen-Ho |
title |
A Multivariate Fuzzy Hidden Markov Model For Time Series Forecasting |
title_short |
A Multivariate Fuzzy Hidden Markov Model For Time Series Forecasting |
title_full |
A Multivariate Fuzzy Hidden Markov Model For Time Series Forecasting |
title_fullStr |
A Multivariate Fuzzy Hidden Markov Model For Time Series Forecasting |
title_full_unstemmed |
A Multivariate Fuzzy Hidden Markov Model For Time Series Forecasting |
title_sort |
multivariate fuzzy hidden markov model for time series forecasting |
publishDate |
2014 |
url |
http://ndltd.ncl.edu.tw/handle/66819458822895181177 |
work_keys_str_mv |
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