Forecasting Trending Tiem Series: A Comparative Analysis of Nonlinear Models_

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題名：	Forecasting Trending Tiem Series: A Comparative Analysis of Nonlinear Models
書刊名：	正修學報
作者：	吳正義／吳柏林
出版日期：	1995
卷期：	8
頁次：	頁117-131
主題關鍵詞：	時間數列趨勢；狀態空間；指數成長率；自由模式；神經網路；Trending time series；State space；Exponential growth rate；RGARMA；Model free；Neural networks
原始連結：	連回原系統網址
相關次數：	被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0) 排除自我引用:0 共同引用:0 點閱:15

　　　　　傳統上，時間數列分析大都在穩定隨機過程上考慮。但是在實證研究時，很多趨勢型時間數列資料呈現穩定成長之非線型圖形，如指數成長。本研究即針對此趨勢型時間數列提出三種模式建構與預測分析方法：（ 1 ）狀態空間方程式（ 2 ） REGARIMAModel （ 3 ）神經網路學習法；詳細說明其特性。最後我們以中央政府總支出與教科文總支出做一實證研究，並比較此三種模式建構與預測分析方法預測效果之優劣。

以文找文

　　　　　Conventionally the research on time series analysis has focused upon the modeling of dynamic data under the assumptions of linearity and stationarity. While in the socioeconomic system, many time series are steadily increasing with time and exhibit certain nonlinear shapes such as exponential curve. In this paper, we introduce three alternative techniques for modeling trending data. One is to use the state space representation. Another is to use the reltive growth rate of ARMA models. The other is to use the neurocomputing as the learning and forecasting tools by the neural networks. Finally, we present an illustrative example about the annual expenditures on government and expenditures on science-education-culture of Taiwan and compare the forecasting performance by abov methods.

以文找文

期刊論文
1.	Harrison, P. J.、Stevens, C. F.(1976)。Bayesian forecasting (with Discussion)。J. R. Statist Soc. B，38，205-247。
2.	Tegene, Abebayehu(1991)。Kalman Filter and the Demand for Cigarettes。Applied Economics，23(7)，1175-1182。
3.	Granger, C. M. J.(1991)。Developments in the Nonlinear Analysis of Economic Series。Scand. J. of Economics，93(2)，263-276。
4.	Amari, S.(1990)。Mathematical Fundations of Neurocomputing。Proceeding of the IEEE，78(9)，1443-1463。
5.	Bohara, A. K.、Sauer, C.(1992)。Competing Macro-hypotheses in the United States: A Kalman Filtering Approach。Applied Economics，24，389-399。
6.	Chavas, J-P.(1983)。Structure Change in the demand for meat。American Journal of agriculture IN Economics，65，148-53。
7.	Cybenko, G.(1989)。Approximation by superposition of a sigmiodal function。Mathematics of control, signals and systems，2，303-314。
8.	Funahashi, K. I.(1989)。On the approximate of continuous mappings by neural networks。Neural Networks，2，183-192。
9.	Harvey, A. C.、Todd, P. H. J.(1983)。Forecasting economic time series with structural and Box-Jenkins models: A case study with comments。J. of Business and Economic Stat.，1，299-315。
10.	HeChtt-Nielsen, R.(1989)。Neurocomputing。IEEE Spectrum，1989(Mar.)，36-41。
11.	Hornik, Kurt(1991)。Approximation Capabilities of Multilayer Feedforward Networks。Neural Networks，4(2)，251-257。
12.	Hotelling, H.(1927)。Differential Equations Subject to Error and Population。J. Amer. Statis. Assoc，22，283-314。
13.	Kalman, R. E.(1960)。A New Approach to linear filtering and prediction problems。Journal of Basic Engineering，82(1)，34-45。
14.	Kitagawa, Genshiro、Gresch, Will(1984)。A Smoothness Priors Modeling of Time Series with Trend and Seasonity。Journal of the American Statistical Association，79(386)，378-389。
15.	Kolen, J. F.、Goel, A. K.(1991)。Learning in Parallel Distributed Processing Networks: Computational Complexity and Information Content。IEEE Transactionson Systems, Man, and Cybernetics，21(2)，359-367。
16.	Verhulst, P. F.(1838)。Recherches mathematiques sur la loi d'accroi sement de la population。Nouveaux memoirs de l'ac Bruxelles，18，1-38。
17.	Wu, B.、Chan, D.(1992)。Budge-Planning, Forecasting Control of the Taiwan Government expenditure。The National Chengchi University Journal，64，87-104。

圖書
1.	Groberg, S.(1988)。Studies of Mind and Brain: Neural principles of learning, Perception, Development, Cognition and Motor Control。Boston, MA：Reidel。
2.	Kosko, B.(1992)。Neural Networks for Signal Processing。Englewood Cliffs, NJ：Prentice Hall。
3.	Ramey, J.(1989)。Neural Computing。Pittsburgh：NeuralWare, Inc.。
4.	Ross, G. J. S.(1990)。Nonlinear Estimation。New York：Springer-Verlag。
5.	Lawless, J. F.(1982)。Statistical Models and Methods for Lifetime Data。New York：John Wiley and Sons。
6.	Box, G. E. P.、Jenkins, G. M.、Reinsel, G. C.(1976)。Time Series Analysis: Forecasting and Control。San Francisco：Holden-Day。
7.	Wei, William W. S.(1990)。Time series analysis: Univariate and multivariate methods。Addison-Wesley Inc.。

單篇論文
1.	Lapedes, A.，Farber, R.(1988)。How Neural Nets Work，Theoretical Division, Los Alamos National Laboratory。

圖書論文
1.	Harvey, A. C.、Phillips, G. D. A.(1981)。The Estimation of Regression Models with Time-Varying Parameters。Games, Economic Dynamics, and Time Series Analysis。Wilen：Physica-Verlag。
2.	Akaike, H.(1976)。Canonical correlation analysis of time series and the use of an information criterion。System Identification: advances and case studies。New York：Academic Press。
3.	Gordon, K.、Smith, A. F. M.(1988)。Modeling and Monitoring Discontinuous Changes in Time series。Bayesian Analysis of Time series and Dynamic Linear Models。New York：Marcel Dekker。
4.	Jones, R. H.(1984)。Fitting Multivariate Models to unequally spaced data。Time Series Analysis of Irregularly Observed Data。New York：Springer。
5.	Rausser, G. C.、Mundlak, Y.、Johnson, S. R.(1983)。Structural Change, Parameter variation, and Forecasting。New Directions in Econometric Modeling and forecasting in U.S. Agriculture。New York：American Elsevier Pub. Co.。
6.	Shumway, R. H.(1985)。Time Series in the soil Sciences: Is There Life .After Kxiging? Soil Spatial。Soil Spatial Variability。Pudoc Wageningen：The Netherlands。

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