ARIMA-BP times series neural networks
碩士 === 中華大學 === 資訊管理學系(所) === 96 === In this paper we proposed an ARIMA-BPN algorithm combining advantages of ARIMA and Back-propagation networks (BPN). The algorithm is based on BPN and its inputs are the same as ARIMA. It can generate a non-linear function to create an accurate model to predict...
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| Format: | Others |
| Language: | zh-TW |
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2007
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| Online Access: | http://ndltd.ncl.edu.tw/handle/31461568317667656927 |
| Summary: | 碩士 === 中華大學 === 資訊管理學系(所) === 96 === In this paper we proposed an ARIMA-BPN algorithm combining advantages of ARIMA and Back-propagation networks (BPN). The algorithm is based on BPN and its inputs are the same as ARIMA. It can generate a non-linear function to create an accurate model to predict time series. The BPN algorithm must be modified because the residuals would be changed when the weights were changed during continuously training BPN. That is we will use the continuously updated residuals as inputs. This study examined 6 artificial designed cases and 4 real world cases to evaluate the abilities of the ARIMA, BPN, and ARIMA-BPN. The results sowed that ARIMA-BPN is the most accurate methods in some cases.
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