Forecasting for nonstationary time series a neural networks approach
碩士 === 國立政治大學 === 統計研究所 === 80 === CONVENTIONAL TIME SERIES ANALYSIS DEPENDS HEAVILY ON THE TWIN SUMPTIONS OF LINEARITY AND STATIONARITY. HOWEVER, THERE ARE CERTAIN CASES WHERE SAMPLED DATA TEND TO VIOLATE THE ASSUMPTIONS. IN THIS PAPER,...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
1992
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| Online Access: | http://ndltd.ncl.edu.tw/handle/45027944180024261207 |
| Summary: | 碩士 === 國立政治大學 === 統計研究所 === 80 === CONVENTIONAL TIME SERIES ANALYSIS DEPENDS HEAVILY ON THE TWIN SUMPTIONS
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.
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