A comparison of seasonal time series models for forecasting the energy consumption in Taiwan

碩士 === 淡江大學 === 數學學系碩士班 === 96 === Recently, the energy price keeps increasing.Both the demand and the consumption are on the rise.Due to these scenarios,this essay will try to predict the energy consumption in Taiwan,hoping to get a better grasp of the future trend.We will use the following four mo...

Full description

Bibliographic Details
Main Authors:	Chian-Shan Huang, 黃千珊
Other Authors:	Jyh-Shyang Wu
Format:	Others
Language:	zh-TW
Published:	2008
Online Access:	http://ndltd.ncl.edu.tw/handle/26773247449379626795

id	ndltd-TW-096TKU05479004
record_format	oai_dc
spelling	ndltd-TW-096TKU054790042016-05-18T04:13:37Z http://ndltd.ncl.edu.tw/handle/26773247449379626795 A comparison of seasonal time series models for forecasting the energy consumption in Taiwan 比較季節性時間序列預測模型－台灣地區能源消費之實證研究 Chian-Shan Huang 黃千珊碩士淡江大學數學學系碩士班 96 Recently, the energy price keeps increasing.Both the demand and the consumption are on the rise.Due to these scenarios,this essay will try to predict the energy consumption in Taiwan,hoping to get a better grasp of the future trend.We will use the following four models for prediction,and they are Seasonal Autoregressive Integrated Moving Average Models(SARIMA),Regression Models with Time Series Errors (RMTSE),Back-propagation Network(BPN),and hybrid SARIMA and BPN(SARIMABP).The findings discovered that,at that time series of graph the sequence shook obviously uses BPN to be able to obtain a better forecast,otherwise,the graph shook steadily, used SARIMA to be able to obtain a better forecast,and adopt the mixing model to be able to improve the forecast error. Jyh-Shyang Wu 伍志祥 2008 學位論文 ; thesis 59 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 淡江大學 === 數學學系碩士班 === 96 === Recently, the energy price keeps increasing.Both the demand and the consumption are on the rise.Due to these scenarios,this essay will try to predict the energy consumption in Taiwan,hoping to get a better grasp of the future trend.We will use the following four models for prediction,and they are Seasonal Autoregressive Integrated Moving Average Models(SARIMA),Regression Models with Time Series Errors (RMTSE),Back-propagation Network(BPN),and hybrid SARIMA and BPN(SARIMABP).The findings discovered that,at that time series of graph the sequence shook obviously uses BPN to be able to obtain a better forecast,otherwise,the graph shook steadily, used SARIMA to be able to obtain a better forecast,and adopt the mixing model to be able to improve the forecast error.
author2	Jyh-Shyang Wu
author_facet	Jyh-Shyang Wu Chian-Shan Huang 黃千珊
author	Chian-Shan Huang 黃千珊
spellingShingle	Chian-Shan Huang 黃千珊 A comparison of seasonal time series models for forecasting the energy consumption in Taiwan
author_sort	Chian-Shan Huang
title	A comparison of seasonal time series models for forecasting the energy consumption in Taiwan
title_short	A comparison of seasonal time series models for forecasting the energy consumption in Taiwan
title_full	A comparison of seasonal time series models for forecasting the energy consumption in Taiwan
title_fullStr	A comparison of seasonal time series models for forecasting the energy consumption in Taiwan
title_full_unstemmed	A comparison of seasonal time series models for forecasting the energy consumption in Taiwan
title_sort	comparison of seasonal time series models for forecasting the energy consumption in taiwan
publishDate	2008
url	http://ndltd.ncl.edu.tw/handle/26773247449379626795
work_keys_str_mv	AT chianshanhuang acomparisonofseasonaltimeseriesmodelsforforecastingtheenergyconsumptionintaiwan AT huángqiānshān acomparisonofseasonaltimeseriesmodelsforforecastingtheenergyconsumptionintaiwan AT chianshanhuang bǐjiàojìjiéxìngshíjiānxùlièyùcèmóxíngtáiwāndeqūnéngyuánxiāofèizhīshízhèngyánjiū AT huángqiānshān bǐjiàojìjiéxìngshíjiānxùlièyùcèmóxíngtáiwāndeqūnéngyuánxiāofèizhīshízhèngyánjiū AT chianshanhuang comparisonofseasonaltimeseriesmodelsforforecastingtheenergyconsumptionintaiwan AT huángqiānshān comparisonofseasonaltimeseriesmodelsforforecastingtheenergyconsumptionintaiwan
_version_	1718271570178211840

A comparison of seasonal time series models for forecasting the energy consumption in Taiwan

Similar Items