A General Framework for Enhancing Prediction Performance on Time Series Data

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 101 === Traditionally, researchers apply the latest data to predict the near future of Time Series Data prediction. However, we proposed a novel framework to use not only latest data but also potential accurate predicted results. And it also be able to predict much fur...

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Main Authors:	Chin-Hui Chen, 陳晉暉
Other Authors:	鄭卜壬
Format:	Others
Language:	zh-TW
Published:	2013
Online Access:	http://ndltd.ncl.edu.tw/handle/00616418460500687137

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spelling	ndltd-TW-101NTU053920522016-03-16T04:15:17Z http://ndltd.ncl.edu.tw/handle/00616418460500687137 A General Framework for Enhancing Prediction Performance on Time Series Data 增進時序資料預測效能之一般化模型 Chin-Hui Chen 陳晉暉碩士國立臺灣大學資訊工程學研究所 101 Traditionally, researchers apply the latest data to predict the near future of Time Series Data prediction. However, we proposed a novel framework to use not only latest data but also potential accurate predicted results. And it also be able to predict much further results for enhancing the prediction. The framework adopts generic predict methods and extract specific features ac- cording to the data property. Three type of feature sets are designed to capture the Statistic, Reliability and Periodicity of the Time Series Data. Short-Term and Long-Term Prediction Enhancement algorithms are also introduced to im- prove the prediction performance. The experiments show that Short-Term En- hancement increases the accuracy of +20.04% and Long-Term Enhancement +9.59% compared to well-known baseline approaches, ARIMA and HW-ES. 鄭卜壬 2013 學位論文 ; thesis 33 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 國立臺灣大學 === 資訊工程學研究所 === 101 === Traditionally, researchers apply the latest data to predict the near future of Time Series Data prediction. However, we proposed a novel framework to use not only latest data but also potential accurate predicted results. And it also be able to predict much further results for enhancing the prediction. The framework adopts generic predict methods and extract specific features ac- cording to the data property. Three type of feature sets are designed to capture the Statistic, Reliability and Periodicity of the Time Series Data. Short-Term and Long-Term Prediction Enhancement algorithms are also introduced to im- prove the prediction performance. The experiments show that Short-Term En- hancement increases the accuracy of +20.04% and Long-Term Enhancement +9.59% compared to well-known baseline approaches, ARIMA and HW-ES.
author2	鄭卜壬
author_facet	鄭卜壬 Chin-Hui Chen 陳晉暉
author	Chin-Hui Chen 陳晉暉
spellingShingle	Chin-Hui Chen 陳晉暉 A General Framework for Enhancing Prediction Performance on Time Series Data
author_sort	Chin-Hui Chen
title	A General Framework for Enhancing Prediction Performance on Time Series Data
title_short	A General Framework for Enhancing Prediction Performance on Time Series Data
title_full	A General Framework for Enhancing Prediction Performance on Time Series Data
title_fullStr	A General Framework for Enhancing Prediction Performance on Time Series Data
title_full_unstemmed	A General Framework for Enhancing Prediction Performance on Time Series Data
title_sort	general framework for enhancing prediction performance on time series data
publishDate	2013
url	http://ndltd.ncl.edu.tw/handle/00616418460500687137
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A General Framework for Enhancing Prediction Performance on Time Series Data

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