Time series forecasting using a hybrid ARIMA and neural network model

Neurocomputing - Tập 50 - Trang 159-175 - 2003
G.Peter Zhang1
1Department of Management, J. Mack Robinson College of Business, Georgia State University, University Plaza, Atlanta, GA 30303, USA

Tóm tắt

Từ khóa


Tài liệu tham khảo

Bates, 1969, The combination of forecasts, Oper. Res. Q., 20, 451, 10.1057/jors.1969.103

Box, 1970

Campbell, 1977, A survey of statistical work on the MacKenzie River series of annual Canadian lynx trappings for the years 1821–1934, and a new analysis, J. R. Statist. Soc. Ser. A, 140, 411, 10.2307/2345277

Chatfield, 1988, What is the ‘best’ method of forecasting?, J. Appl. Statist., 15, 19, 10.1080/02664768800000003

Chatfield, 1996, Model uncertainty and forecast accuracy, J. Forecasting, 15, 495, 10.1002/(SICI)1099-131X(199612)15:7<495::AID-FOR640>3.0.CO;2-O

Clemen, 1989, Combining forecasts: a review and annotated bibliography with discussion, Int. J. Forecasting, 5, 559, 10.1016/0169-2070(89)90012-5

Cottrell, 1995, Neural modeling for time series: a statistical stepwise method for weight elimination, IEEE Trans. Neural Networks, 6, 1355, 10.1109/72.471372

De Gooijer, 1992, Some recent developments in non-linear time series modelling, testing, and forecasting, Int. J. Forecasting, 8, 135, 10.1016/0169-2070(92)90115-P

De Groot, 1991, Analysis of univariate time series with connectionist nets: a case study of two classical examples, Neurocomputing, 3, 177, 10.1016/0925-2312(91)90040-I

Denton, 1995, How good are neural networks for causal forecasting?, J. Bus. Forecasting, 14, 17

Engle, 1982, Autoregressive conditional heteroscedasticity with estimates of the variance of U.K. inflation, Econometrica, 50, 987, 10.2307/1912773

Fildes, 1995, The impact of empirical accuracy studies on time series analysis and forecasting, Int. Statist. Rev., 63, 289, 10.2307/1403481

Ginzburg, 1994, Combined neural networks for time series analysis, Adv. Neural Inf. Process. Systems, 6, 224

Granger, 1978

Granger, 1989, Combining forecasts—Twenty years later, J. Forecasting, 8, 167, 10.1002/for.3980080303

Hipel, 1994

Hornik, 1990, Using multi-layer feedforward networks for universal approximation, Neural Networks, 3, 551, 10.1016/0893-6080(90)90005-6

Hung, 1993, Training neural networks with the GRG2 nonlinear optimizer, Eur. J. Oper. Res., 69, 83, 10.1016/0377-2217(93)90093-3

Jenkins, 1982, Some practical aspects of forecasting in organisations, J. Forecasting, 1, 3, 10.1002/for.3980010103

Krogh, 1995, Neural network ensembles, cross validation, and active learning, Adv. Neural Inf. Process., 7, 231

Luxhoj, 1996, A hybrid econometric-neural network modeling approach for sales forecasting, Int. J. Prod. Econ., 43, 175, 10.1016/0925-5273(96)00039-4

Makridakis, 1989, Why combining works?, Int. J. Forecasting, 5, 601, 10.1016/0169-2070(89)90017-4

Makridakis, 1982, The accuracy of extrapolation (time series) methods: results of a forecasting competition, J. Forecasting, 1, 111, 10.1002/for.3980010202

Makridakis, 1993, The M-2 competition: a real-life judgmentally based forecasting study, Int. J. Forecasting, 9, 5, 10.1016/0169-2070(93)90044-N

Markham, 1998, The effect of sample size and variability of data on the comparative performance of artificial neural networks and regression, Comput. Oper. Res., 25, 251, 10.1016/S0305-0548(97)00074-9

McKenzie, 1984, General exponential smoothing and the equivalent ARMA process, J. Forecasting, 3, 333, 10.1002/for.3980030312

Meese, 1983, Empirical exchange rate models of the seventies: do they fit out of samples?, J. Int. Econ., 14, 3, 10.1016/0022-1996(83)90017-X

Newbold, 1974, Experience with forecasting univariate time series and the combination of forecasts (with discussion), J. R. Statist. Soc. Ser. A, 137, 131, 10.2307/2344546

Palm, 1992, To combine or not to combine? Issues of combining forecasts, J. Forecasting, 11, 687, 10.1002/for.3980110806

Pelikan, 1992, Power consumption in West-Bohemia: improved forecasts with decorrelating connectionist networks, Neural Network World, 2, 701

Perrone, 1993, When networks disagree: ensemble method for hybrid neural networks, 126

Reid, 1968, Combining three estimates of gross domestic product, Economica, 35, 431, 10.2307/2552350

Subba Rao, 1984, Vol. 24

R. Sharda, R.B. Patil, Neural networks as forecasting experts: an empirical test, in: Proceedings of the International Joint Conference on Neural Networks, Washington, D.C., Vol. 2, 1990, pp. 491–494.

Subramanian, 1993, A GRG2-based system for training neural networks: design and computational experience, ORSA J. Comput., 5, 386, 10.1287/ijoc.5.4.386

Tang, 1991, Time series forecasting using neural networks vs Box–Jenkins methodology, Simulation, 57, 303, 10.1177/003754979105700508

Tang, 1993, Feedforward neural nets as models for time series forecasting, ORSA J. Comput., 5, 374, 10.1287/ijoc.5.4.374

Tong, 1983

Wedding II, 1996, Time series forecasting by combining RBF networks, certainty factors, and the Box–Jenkins model, Neurocomputing, 10, 149, 10.1016/0925-2312(95)00021-6

Winkler, 1989, Combining forecasts: a philosophical basis and some current issues, Int. J. Forecasting, 5, 605, 10.1016/0169-2070(89)90018-6

Wold, 1938

Yule, 1926, Why do we sometimes get nonsense-correlations between time series? A study in sampling and the nature of time series, J. R. Statist. Soc., 89, 1, 10.2307/2341482

Zhang, 1998, Forecasting with artificial neural networks: the state of the art, Int. J. Forecasting, 14, 35, 10.1016/S0169-2070(97)00044-7

Zhang, 2001, A simulation study of artificial neural networks for nonlinear time-series forecasting, Comput. Oper. Res., 28, 381, 10.1016/S0305-0548(99)00123-9

Elkateb, 1998, A comparative study of medium-weather-dependent load forecasting using enhanced artificial/fuzzy neural network and statistical techniques, Neurocomputing, 23, 3, 10.1016/S0925-2312(98)00076-9

Al-Saba, 1999, Artificial neural networks as applied to long-term demand forecasting, Artif. Intell. Eng., 13, 189, 10.1016/S0954-1810(98)00018-1

Hwang, 2001, Insights into neural-network forecasting time series corresponding to ARMA (p, q) structures, Omega, 29, 273, 10.1016/S0305-0483(01)00022-6