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非平穩性時間數列預測 / Forecasting for nonstationary time series a neural networks approach

Conventional time series analysis depends heavily on the twin assumptions of linearity and stationarity. However; there are certain cases where sampled data tend to violate the assumptions. In this paper, we use neural networks technology to explore the situation when the assumptions of linearity and stationarity are failed. At the end of the paper, we discuss an illustrative example about the annual expenditures of government and science-education-culture of R.O.C.

Identiferoai:union.ndltd.org:CHENGCHI/B2002004642
Creators于健, YU, JIAN
Publisher國立政治大學
Source SetsNational Chengchi University Libraries
Detected LanguageEnglish
Typetext
RightsCopyright © nccu library on behalf of the copyright holders

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