Forecasting with artificial neural networks:
Tóm tắt
Từ khóa
Tài liệu tham khảo
Aiken, 1995, A neural network for predicting total industrial production, Journal of End User Computing, 7, 19
Akaike, 1974, A new look at the statistical model identification, IEEE Transactions on Automatic Control, 19, 716, 10.1109/TAC.1974.1100705
Amari, 1994, A comment on “Neural networks: A review from a statistical perspective”, Statistical Science, 9, 31, 10.1214/ss/1177010639
Amirikian, 1994, What size network is good for generalization of a specific task of interest?, Neural Networks, 7, 321, 10.1016/0893-6080(94)90026-4
Arizmendi, 1993, Time series prediction with neural nets: Application to airborne pollen forecasting, International Journal of Biometeorology, 37, 139, 10.1007/BF01212623
Armstrong, 1995, Correspondence: On the selection of error measures for comparisons among forecasting methods, Journal of Forecasting, 14, 67, 10.1002/for.3980140106
Azoff, E.M., 1994. Neural Network Time Series Forecasting of Financial Markets. John Wiley and Sons, Chichester.
Bacha, H., Meyer, W., 1992. A neural network architecture for load forecasting. In: Proceedings of the IEEE International Joint Conference on Neural Networks, 2, pp. 442–447.
Bakirtzis, 1995, Short term load forecasting using fuzzy neural networks, IEEE Transactions on Power Systems, 10, 1518, 10.1109/59.466494
Balestrino, 1994, Time series analysis by neural networks: Environmental temperature forecasting, Automazione e Strumentazione, 42, 81
Barker, 1990, Analyzing financial health: Integrating neural networks and expert systems, PC AI, 4, 24
Barron, 1994, A comment on “Neural networks: A review from a statistical perspective”, Statistical Science, 9, 33, 10.1214/ss/1177010640
Bataineh, 1996, An expert system for unit commitment and power demand prediction using fuzzy logic and neural networks, Expert Systems, 13, 29, 10.1111/j.1468-0394.1996.tb00281.x
Battiti, 1992, First- and second-order methods for learning: Between steepest descent and Newton's method, Neural Computation, 4, 141, 10.1162/neco.1992.4.2.141
Baum, 1989, What size net gives valid generalization?, Neural Computation, 1, 151, 10.1162/neco.1989.1.1.151
Bergerson, K., Wunsch, D.C., 1991. A commodity trading model based on a neural network–expert system hybrid. In: Proceedings of the IEEE International Conference on Neural Networks, Seattle, WA, pp. 1289–1293.
Borisov, 1995, Prediction of a continuous function with the aid of neural networks, Automatic Control and Computer Sciences, 29, 39
Bowerman, B.L., O'Connell, R.T., 1993. Forecasting and Time Series: An Applied Approach, 3rd ed. Duxbury Press, Belmont, CA.
Box, G.E.P., Jenkins, G.M., 1976. Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco, CA.
Brace, M.C., Schmidt, J., Hadlin, M., 1991. Comparison of the forecasting accuracy of neural networks with other established techniques. In: Proceedings of the First Forum on Application of Neural Networks to Power Systems, Seattle, WA, pp. 31–35.
Caire, P., Hatabian, G., Muller, C., 1992. Progress in forecasting by neural networks. In: Proceedings of the International Joint Conference on Neural Networks, 2, pp. 540–545.
Chakraborty, 1992, Forecasting the behavior of multivariate time series using neural networks, Neural Networks, 5, 961, 10.1016/S0893-6080(05)80092-9
Chan, D.Y.C., Prager, D., 1994. Analysis of time series by neural networks. In: Proceedings of the IEEE International Joint Conference on Neural Networks, 1, pp. 355–360.
Chan, 1986, On tests for non-linearity in time series analysis, Journal of Forecasting, 5, 217, 10.1002/for.3980050403
Chang, I., Rapiraju, S., Whiteside, M., Hwang, G., 1991. A neural network to time series forecasting. In: Proceedings of the Decision Science Institute., 3, pp. 1716–1718.
Chatfield, 1993, Neural networks: Forecasting breakthrough or passing fad?, International Journal of Forecasting, 9, 1, 10.1016/0169-2070(93)90043-M
Chen, C.H., 1994. Neural networks for financial market prediction. In: Proceedings of the IEEE International Conference on Neural Networks, 2, pp. 1199–1202.
Chen, S.T., Yu, D.C., Moghaddamjo, A.R., 1991. Weather sensitive short-term load forecasting using nonfully connected artificial neural network. In: Proceedings of the IEEE/Power Engineering Society Summer Meeting, 91 SM 449–8 PWRS.
Chen, 1995, Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems, IEEE Transactions on Neural Networks, 6, 911, 10.1109/72.392253
Cheng, 1994, Neural networks: A review from a statistical perspective, Statistical Science, 9, 2, 10.1214/ss/1177010638
Chester, D.L., 1990. Why two hidden layers are better than one? In: Proceedings of the International Joint Conference on Neural Networks, pp. 1265–1268.
Cheung, 1996, Maximum-entropy approach to identify time-series lag structure for developing intelligent forecasting systems, International Journal of Computational Intelligence and Organization, 1, 94
Chiang, 1996, A neural network approach to mutual fund net asset value forecasting, Omega, 24, 205, 10.1016/0305-0483(95)00059-3
Chng, 1996, Gradient radial basis function networks for nonlinear and nonstationary time series prediction, IEEE Transactions on Neural Networks, 7, 190, 10.1109/72.478403
Chu, 1994, Neural network system for forecasting method selection, Decision Support Systems, 12, 13, 10.1016/0167-9236(94)90071-X
Clements, 1993, On the limitations of comparing mean square forecast errors, Journal of Forecasting, 12, 615
Coleman, K.G., Graettinger, T.J., Lawrence, W.F., 1991. Neural networks for bankruptcy prediction: The power to solve financial problems. AI Review 5, July/August, 48–50.
Connor, 1994, Recurrent neural networks and robust time series prediction, IEEE Transaction on Neural Networks, 51, 240, 10.1109/72.279188
Cottrell, 1995, Neural modeling for time series: a statistical stepwise method for weight elimination, IEEE Transactions on Neural Networks, 6, 1355, 10.1109/72.471372
Cromwell, J.B., Labys, W.C., Terraza, M., 1994. Univariate Tests for Time Series Models. Saga Publications, Thousand Oaks.
Cybenko, G., 1988. Continuous Valued Neural Networks with Two Hidden Layers are Sufficient. Technical Report, Tuft University.
Cybenko, 1989, Approximation by superpositions of a sigmoidal function, Mathematical Control Signals Systems, 2, 303, 10.1007/BF02551274
Dash, 1995, Fuzzy neural networks for time-series forecasting of electric load, IEE Proceedings – Generation, Transmission and Distribution, 142, 535, 10.1049/ip-gtd:19951807
De Gooijer, 1992, Some recent developments in non-linear time series modelling, testing, and forecasting, International Journal of 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
Delyon, 1995, Accuracy analysis for wavelet approximations, IEEE Transactions on Neural Networks, 6, 332, 10.1109/72.363469
Denton, J.W., 1995. How good are neural networks for causal forecasting? The Journal of Business Forecasting 14 (2), Summer, 17–20.
Deppisch, 1991, Hierarchical training of neural networks and prediction of chaotic time series, Physics Letters, 158, 57, 10.1016/0375-9601(91)90340-E
Donaldson, 1996, Forecasting combining with neural networks, Journal of Forecasting, 15, 49, 10.1002/(SICI)1099-131X(199601)15:1<49::AID-FOR604>3.0.CO;2-2
Duliba, K.A., Contrasting neural nets with regression in predicting performance in the transportation industry. In: Proceedings of the Annual IEEE International Conference on Systems Sciences., 25, pp. 163–170.
Dutta, S., Shekhar, S., 1988. Bond rating: A non-conservative application of neural networks. In: Proceedings of the IEEE International Conference on Neural Networks. San Diego, California, 2, pp. 443–450.
El-Sharkawi, M.A., Oh, S., Marks, R.J., Damborg, M.J., Brace, C.M., 1991. Short-term electric load forecasting using an adaptively trained layered perceptron. In: Proceedings of the 1st International Forum on Application of Neural Networks to Power Systems, 3–6.
Engelbrecht, A.P., Cloete, I., Geldenhuys, J., Zurada, J.M., 1994. Automatic scaling using gamma learning for feedforward neural networks. In: Anderson, D.Z. (Ed.), Neural Information Processing Systems, American Institute of Physics, New York, pp. 374–381.
Engle, 1982, Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation, Econometrica, 50, 987, 10.2307/1912773
Ezugwu, 1995, Too-wear prediction using artificial neural networks, Journal of Materials Processing Technology, 49, 255, 10.1016/0924-0136(94)01351-Z
Falhman, S., 1989. Faster-learning variations of back-propagation: An empirical study. In: Touretzky, D., Hinton, G., Sejnowski, T., (Eds.), Proceedings of the 1988 Connectionist Models Summer School, pp. 38–51.
Fishwick, P.A., 1989. Neural network models in simulation: A comparison with traditional modeling approaches. In: Proceedings of Winter Simulation Conference, Washington, D.C., pp. 702–710.
Fletcher, 1993, Forecasting with neural networks – An application using bankruptcy data, Information and Management, 24, 159, 10.1016/0378-7206(93)90064-Z
Fletcher, R., 1987. Practical Methods of Optimization, 2nd ed. John Wiley, Chichester.
Foster, 1992, Neural network forecasting of short, noisy time series, Computers and Chemical Engineering, 16, 293, 10.1016/0098-1354(92)80049-F
Funahashi, 1989, On the approximate realization of continuous mappings by neural networks, Neural Networks, 2, 183, 10.1016/0893-6080(89)90003-8
Gately, E., 1996. Neural Networks for Financial Forecasting. John Wiley, New York.
Geman, 1992, Neural networks and the bias/variance dilemma, Neural Computation, 5, 1, 10.1162/neco.1992.4.1.1
Gent, C.R., Sheppard, C.P., 1992. Predicting time series by a fully connected neural network trained by back propagation. Computing and Control Engineering Journal 3 (3), May, 109–112.
Gershenfeld, N.A., Weigend, A.S., 1993. The future of time series: learning and understanding. In: Weigend, A.S., Gershenfeld, N.A. (Eds.), Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley, Reading, MA, pp. 1–70.
Ginzburg, I., Horn, D., 1991. Learnability of time series. In: Proceedings of the IEEE International Joint Conference on Neural Networks, 3, pp. 2653–2657.
Ginzburg, 1992, Learning the rule of a time series, International Journal of Neural Systems, 3, 167, 10.1142/S0129065792000140
Ginzburg, 1994, Combined neural networks for time series analysis, Advances in Neural Information Processing Systems, 6, 224
Gorr, 1994, Research prospective on neural network forecasting, International Journal of Forecasting, 10, 1, 10.1016/0169-2070(94)90044-2
Gorr, 1994, Comparative study of artificial neural network and statistical models for predicting student grade point averages, International Journal of Forecasting, 10, 17, 10.1016/0169-2070(94)90046-9
Granger, 1993, Strategies for modelling nonlinear time-series relationships, The Economic Record, 69, 233, 10.1111/j.1475-4932.1993.tb02103.x
Granger, C.W.J., Anderson, A.P., 1978. An Introduction to Bilinear Time Series Models. Vandenhoeck and Ruprecht, Göttingen.
Granger, C.W.J., Terasvirta, T., 1993. Modelling Nonlinear Economic Relationships. Oxford University Press, Oxford.
Grudnitski, 1993, Forecasting S and P and gold futures prices: An application of neural networks, The Journal of Futures Markets, 13, 631, 10.1002/fut.3990130605
Guo, Z., Uhrig, R., 1992. Using genetic algorithm to select inputs for neural networks. In: Proceedings of the Workshop on Combinations of Genetic Algorithms and Neural Networks, COGANN92, pp. 223–234.
Haas, 1995, Prediction of helicopter component loads using neural networks, Journal of the American Helicopter Society, 40, 72, 10.4050/JAHS.40.72
Hann, 1996, Much ado about nothing? Exchange rate forecasting: Neural networks vs. linear models using monthly and weekly data, Neurocomputing, 10, 323, 10.1016/0925-2312(95)00137-9
Happel, 1994, The design and evolution of modular neural network architectures, Neural Networks, 7, 985, 10.1016/S0893-6080(05)80155-8
Hecht-Nielsen, R., 1990. Neurocomputing. Addison-Wesley, Menlo Park, CA.
Hertz, J., Krogh, A., Palmer, R.G., 1991. Introduction to the Theory of Neural Computation. Addison-Wesley, Reading, MA.
Hill, 1994, Artificial neural networks for forecasting and decision making, International Journal of Forecasting, 10, 5, 10.1016/0169-2070(94)90045-0
Hill, 1996, Neural network models for time series forecasts, Management Sciences, 42, 1082, 10.1287/mnsc.42.7.1082
Hinich, 1982, Testing for Gaussianity and linearity of a statistionary time series, Journal of Time Series Analysis, 3, 169, 10.1111/j.1467-9892.1982.tb00339.x
Hinton, G.E., 1992. How neural networks learn from experience, Scientific American, September, 145–151.
Ho, 1992, Short term load forecasting using a multilayer neural network with an adaptive learning algorithm, IEEE Transactions on Power Systems, 7, 141, 10.1109/59.141697
Hopfield, 1982, Neural networks and physical systems with emergent collective computational abilities, Proceedings of the National Academy of the Sciences of the U.S.A., 79, 2554, 10.1073/pnas.79.8.2554
Hornik, 1991, Approximation capabilities of multilayer feedforward networks, Neural Networks, 4, 251, 10.1016/0893-6080(91)90009-T
Hornik, 1993, Some new results on neural network approximation, Neural Networks, 6, 1069, 10.1016/S0893-6080(09)80018-X
Hornik, 1989, Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359, 10.1016/0893-6080(89)90020-8
Hsu, 1991, Design of artificial neural networks for short-term load forecasting, Part I: selforganising feature maps for day type identification, IEE Proceedings-C: Generation, Transmission and Distribution, 138, 407, 10.1049/ip-c.1991.0051
Hsu, 1991, Design of artificial neural networks for short-term load forecasting, Part II: Multilayer feedforward networks for peak load and valley load forecasting, IEE Proceedings- C; Generation, Transmission and Distribution, 138, 414, 10.1049/ip-c.1991.0052
Hu, M.J.C., 1964. Application of the adaline system to weather forecasting. Master Thesis, Technical Report 6775-1, Stanford Electronic Laboratories, Stanford, CA, June.
Hung, 1993, Training neural networks with the GRG2 nonlinear optimizer, European Journal of Operational Research, 69, 83, 10.1016/0377-2217(93)90093-3
Huntley, 1991, Neural nets: An approach to the forecasting of time series, Social Science Computer Review, 9, 27, 10.1177/089443939100900104
Hush, D.R., Horne, B.G., 1993. Progress in supervised neural networks: What's new since Lippmann? IEEE Signal Processing Magazine, January, 8–38.
Hwang, J.N. and S. Moon, 1991. Temporal difference method for multi-step prediction: Application to power load forecasting. In: Proceedings of the First Forum on Application of Neural Networks to Power Systems, pp. 41–45.
Irie, B., Miyake, S., 1988. Capabilities of three-layered perceptrons. In: Proceedings of the IEEE International Conference on Neural Networks, I, pp. 641–648.
Jacobs, 1988, Increased rates of convergence through learning rate adaptation, Neural Networks, 1, 295, 10.1016/0893-6080(88)90003-2
Jhee, 1992, A neural network approach for the identification of the Box-Jenkins model, Network: Computation in Neural Systems, 3, 323, 10.1088/0954-898X/3/3/005
Jones, 1993, Genetic algorithms and their applications to the design of neural networks, Neural Computing and Applications, 1, 32, 10.1007/BF01411373
Jones, R.D., Lee, Y.C., Barnes, C.W., Flake, G.W., Lee, K., Lewis, P.S., et al., 1990. Function approximation and time series prediction with neural networks. In: Proceedings of the IEEE International Joint Conference on Neural Networks, San Diego, CA, 1, pp. 649–665
Kaastra, 1995, Forecasting futures trading volume using neural networks, The Journal of Futures Markets, 15, 953, 10.1002/fut.3990150806
Kang, S., 1991. An Investigation of the Use of Feedforward Neural Networks for Forecasting. Ph.D. Thesis, Kent State University.
Karnin, 1990, A simple procedure for pruning back-propagation trained neural networks, IEEE Transactions on Neural Networks, 1, 239, 10.1109/72.80236
Karunanithi, 1994, Neural networks for river flow prediction, Journal of Computing in Civil Engineering, 8, 201, 10.1061/(ASCE)0887-3801(1994)8:2(201)
Keenan, 1985, A Turkey nonadditivity-type test for time series nonlinearity, Biometrika, 72, 39, 10.1093/biomet/72.1.39
Kiartzis, 1995, Short-term load forecasting using neural networks, Electric Power Systems Research, 33, 1, 10.1016/0378-7796(95)00920-D
Kim, 1995, Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems, IEEE Transactions on Power Systems, 10, 1534, 10.1109/59.466492
Kimoto, T., Asakawa, K., Yoda, M., Takeoka, M., 1990. Stock Market prediction system with modular neural networks. In: Proceedings of the IEEE International Joint Conference on Neural Networks. San Diego, California, 2, pp. 11–16.
Klimasauskas, C.C., 1991. Applying neural networks. Part 3: Training a neural network, PC-AI, May/June, 20–24.
Kohonen, 1982, Self-organized formation of topologically correct feature maps, Biological Cybernetics, 43, 59, 10.1007/BF00337288
Kohzadi, 1996, A comparison of artificial neural network and time series models for forecasting commodity prices, Neurocomputing, 10, 169, 10.1016/0925-2312(95)00020-8
Kryzanowski, L., Galler, M., Wright, D.W., 1993. Using artificial neural networks to pick stocks. Financial Analysts Journal, July/August, 21–27.
Kuan, 1995, Forecasting exchange rates using feedforward and recurrent neural networks, Journal of Applied Econometrics, 10, 347, 10.1002/jae.3950100403
Kuan, 1994, Artificial neural networks: an economic perspective, Economic Reviews, 13, 1, 10.1080/07474939408800273
Lachtermacher, 1995, Backpropagation in time-series forecasting, Journal of Forecasting, 14, 381, 10.1002/for.3980140405
Lapedes, A., Farber, R., 1987. Nonlinear signal processing using neural networks: prediction and system modeling. Technical Report LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, NM.
Lapedes, A., Farber, R., 1988. How neural nets work. In: Anderson, D.Z., (Ed.), Neural Information Processing Systems, American Institute of Physics, New York, pp. 442–456.
Lasdon, L.S., Waren, A.D., 1986. GRG2 User's Guide, School of Business Administration, University of Texas at Austin.
Lee, 1994, A two-stage neural network approach for ARMA model identification with ESACF, Decision Support Systems, 11, 461, 10.1016/0167-9236(94)90019-1
Lee, K.Y., Cha, Y.T., Ku, C.C., 1991. A study on neural networks for short-term load forecasting. In: Proceedings of the First Forum on Application of Neural Networks to Power Systems, Seattle, WA, pp. 26–30.
Lee, 1992, Short-term load forecasting using an artificial neural network, IEEE Transactions on Power Systems, 7, 124, 10.1109/59.141695
Lee, 1993, Testing for neglected nonlinearity in time series meodels, Journal of Econometrics, 56, 269, 10.1016/0304-4076(93)90122-L
Lenard, 1995, The application of neural networks and a qualitative response model to the auditor's going concern uncertainty decision, Decision Science, 26, 209, 10.1111/j.1540-5915.1995.tb01426.x
Levin, 1993, Control of nonlinear dynamical systems using neural networks: Controllability and Stabilization, IEEE Transactions on Neural Networks, 4, 192, 10.1109/72.207608
Li, M., Mehrotra, K., Mohan, C., Ranka, S., 1990. Sunspots numbers forecasting using neural networks. In: Proceedings of the 5th IEEE International Symposium on Intelligent Control, pp. 524–529.
Lippmann, R.P., 1987. An introduction to computing with neural nets, IEEE ASSP Magazine, April, 4–22.
Lowe, 1990, Time series prediction by adaptive networks: A dynamical systems perspective, IEE proceedings-F, 138, 17
Lubero, R.G., 1991. Neural networks for water demand time series forecasting. In: Proceedings of the International Workshop on Artificial Neural Networks, pp. 453–460.
Luukkonen, 1988, Testing linearity in univariate time series models, Scandinavian Journal of Statistics, 15, 161
Maasoumi, 1994, Artificial neural networks for some macroeconomic series: A first report, Econometric Reviews, 13, 105, 10.1080/07474939408800276
Makridakis, 1982, The accuracy of extrapolation (time series) methods: Results of a forecasting competition, Journal of Forecasting, 1, 111, 10.1002/for.3980010202
Makridakis, S., Wheelwright, S.C., McGee, V.E., 1983. Forecasting: Methods and Applications, 2nd ed. John Wiley, New York.
Marquez, L., Hill, T., O'Connor, M., Remus, W., 1992. Neural network models for forecast a review. In: IEEE proceedings of the 25th Hawaii International Conference on System Sciences., 4, pp. 494–498.
Masson, 1990, Introduction to computation and learning in artificial neural networks, European Journal of Operational Research, 47, 1, 10.1016/0377-2217(90)90085-P
McLeod, 1983, Diagnostic checking ARMA time series models using squared residual autocorrelations, Journal of Time Series Analysis, 4, 169, 10.1111/j.1467-9892.1983.tb00373.x
Miller, G.F., Todd, P.M., Hegde, S.U., 1989. Designing neural networks using genetic algorithms. In: Schaffer, J.D. (Ed.), Proceedings of the Third International Conference on Genetic Algorithms. Morgon Kaufman, San Francisco, pp. 370–384.
Muller, C., Mangeas, M., 1993. Neural networks and time series forecasting: a theoretical approach. In: IEEE Systems, Man, and Cybernetics Conference Proceedings, pp. 590–594.
Murata, 1994, Network information criterion-determining the number of hidden units for an artificial neural network model, IEEE Transactions on Neural Networks, 5, 865, 10.1109/72.329683
Nam, 1995, Forecasting international airline passenger traffic using neural networks, Logistics and Transportation, 31, 239
Narendra, 1990, Identification and control of dynamical systems using neural networks, IEEE Transactions on Neural Networks, 1, 4, 10.1109/72.80202
Nelson, M., Hill, T., Remus, B., O'Connor, M., 1994. Can neural networks be applied to time series forecasting and learn seasonal patterns: An empirical investigation. In: Proceedings of the Twenty seventh Annual Hawaii International Conference on System Sciences, pp. 649–655.
Odom, M.D., Sharda, R., 1990. A neural network model for bankruptcy prediction. In: Proceedings of the IEEE International Joint Conference on Neural Networks. San Diego, CA, 2, pp. 163–168
Pack, D.C., El-Sharkawi, M.A., Marks II, R.J., 1991a. An adaptively trained neural network. IEEE Transactions on Neural Networks 2 (3), 334–345.
Pack, D.C., El-Sharkawi, M.A., Marks II, R.J., Atlas, L.E., Damborg, M.J., 1991b. Electric load forecasting using an artificial neural network. IEEE Transactions on Power Systems 6 (2), 442–449.
Pankratz, A., 1983. Forecasting with Univariate Box-Jenkins Models: Concepts and Cases. John Wiley, New York.
Park, 1991, Universal approximation using radial basis function networks, Neural Computation, 3, 246, 10.1162/neco.1991.3.2.246
Park, 1993, Approximation and radial basis function networks, Neural Computation, 5, 305, 10.1162/neco.1993.5.2.305
Parker, D.B., 1987. Optimal algorithm for adaptive networks: Second order back propagation, second order direct propagation, and second order Hebbian learning. In: Proceedings of the IEEE International Conference on Neural Networks, 2, pp. 593–600.
Patuwo, 1993, Two-group classification using neural networks, Decision Science, 24, 825, 10.1111/j.1540-5915.1993.tb00491.x
Payeur, 1995, Trajectory prediction for moving objects using artificial neural networks, IEEE Transactions on Industrial Electronic, 42, 147, 10.1109/41.370380
Pelikan, 1992, Power consumption in West-Bohemia: Improved forecasts with decorrelating connectionist networks, Neural Network World, 2, 701
Peng, 1992, Advancement in the application of neural networks for short-term load forecasting, IEEE Transactions on Power Systems, 7, 250, 10.1109/59.141711
Poli, 1994, A neural net model for prediction, Journal of American Statistical Association, 89, 117, 10.2307/2291206
Reed, R., 1993. Pruning algorithms – A survey. IEEE Transactions on Neural Networks, 4 (5), 740–747.
Refenes, A.N., 1993. Constructive learning and its application to currency exchange rate forecasting. In: Trippi, R.R., Turban, E. (Eds.), Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance. Probus Publishing Company, Chicago.
Refenes, A.N., 1995. Neural Networks in the Capital Markets. John Wiley, Chichester.
Refenes, 1994, Stock performance modeling using neural networks: A comparative study with regression models, Neural Networks, 7, 375, 10.1016/0893-6080(94)90030-2
Reilly, D.L., Cooper, L.N., 1990. An overview of neural networks: early models to real world systems. In: Zornetzer, S.F., Davis, J.L., Lau, C. (Eds.), An Introduction to Neural and Electronic Networks. Academic Press, New York, pp. 227–248.
Reynolds, S.B., 1993. A Neural Network Approach to Box-Jenkins Model Identification. Ph.D. Thesis, University of Alabama.
Reynolds, S.B., Mellichamp, J.M., Smith, R.E., 1995. Box-Jenkins forecast model identification. AI Expert, June, 15–28.
Ricardo, S.Z., Guedes, K., Vellasco M., Pacheco, M.A., 1995. Short-term load forecasting using neural nets. In: Mira, J., Sandoval, F. (Eds.), From Natural to Artificial Neural Computation. Springer, Berlin, pp. 1001–1008.
Rioul, 1991, Wavelet and signal processing, IEEE Signal Processing Magazine, 8, 14, 10.1109/79.91217
Ripley, B.D., 1993. Statistical aspects of neural networks. In: Barndorff-Nielsen, O.E., Jensen, J.L., Kendall, W.S. (Eds.), Networks and Chaos-Statistical and Probabilistic Aspects. Chapman and Hall, London, pp. 40–123.
Rissanen, J., 1987. Stochastic complexity (with discussion). Journal of the Royal Statistical Society, B, 49, 223–239 and 252–265.
Rosen, B.E., 1993. Neural network moving averages for time series prediction. In: SPIE, Vol. 1966, Science of Artificial Neural Networks, 2, 448–456.
Roy, 1993, A polynomial time algorithm for the construction and training of a class of multilayer perceptrons, Neural Networks, 6, 535, 10.1016/S0893-6080(05)80057-7
Ruiz-Suarez, 1995, Short-term ozone forecasting by artificial neural networks, Advances in Engineering Software, 23, 143, 10.1016/0965-9978(95)00076-3
Rumelhart, 1986, Learning representations by backpropagating errors, Nature, 323, 533, 10.1038/323533a0
Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. Learning internal representation by back- propagating errors. In: Rumelhart, D.E., McCleland, J.L., the PDP Research Group (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press, MA.
Rumelhart, 1994, The basic ideas in neural networks, Communications of the ACM, 37, 87, 10.1145/175247.175256
Rumelhart, D.E., Durbin, R., Golden, R., Chauvin, Y., 1995. Backpropagation: the basic theory. In: Chauvin, Y., Rumelhart, D.E. (Eds.), Backpropagation: Theory, Architectures, and Applications. Lawrence Erlbaum Associates, New Jersey, pp. 1–34.
Saikkonen, 1988, Lagrange multiplier tests for testing non-linearities in time series models, Scandinavian Journal of Statistics, 15, 55
Salchenkerger, 1992, Neural networks: A new tool for predicting thrift failures, Decision Science, 23, 899, 10.1111/j.1540-5915.1992.tb00425.x
Schiffmann, W., Joost, M., Werner, R., 1993. Application of genetic algorithms to the construction of topologies for multilayer perceptron. In: Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, pp. 675–682.
Schoneburg, 1990, Stock price prediction using neural networks: A project report, Neurocomputing, 2, 17, 10.1016/0925-2312(90)90013-H
Sen, T.K., Oliver, R.J., Sen, N., 1992. Predicting corporate mergers using backpropagating neural networks: A comparative study with logistic models. Working paper, The R.B. Pamplin College of Business, Virginia Tech, Blacksburg, VA.
Shanker, 1996, Effect of data standardization on neural network training, Omega, 24, 385, 10.1016/0305-0483(96)00010-2
Sharda, 1994, Neural networks for the MS/OR analyst: An application bibliography, Interfaces, 24, 116, 10.1287/inte.24.2.116
Sharda, R., Patil, R.B., 1990. Neural networks as forecasting experts: An empirical test. In: Proceedings of the International Joint Conference on Neural Networks. Washington, D.C., 2, pp. 491-494.
Sharda, 1992, Connectionist approach to time series prediction: An empirical test, Journal of Intelligent Manufacturing, 3, 317, 10.1007/BF01577272
Shin, 1995, Ridge polynomial networks, IEEE Transactions on Neural Networks, 6, 610, 10.1109/72.377967
Sietsma, J., Dow, R., 1988. Neural net pruning–Why and how? In: Proceedings of the IEEE International Conference on Neural Networks, 1, pp. 325–333.
Smith, M., 1993. Neural Networks for Statistical Modeling. Van Nostrand Reinhold, New York.
Sohl, 1995, A neural network approach to forecasting model selection, Information and Management, 29, 297, 10.1016/0378-7206(95)00033-4
Srinivasan, 1994, A neural network short-term load forecaster, Electric Power Systems Research, 28, 227, 10.1016/0378-7796(94)90037-X
Subramanian, 1993, A GRG2-based system for training neural networks: Design and computational experience, ORSA Journal on Computing, 5, 386, 10.1287/ijoc.5.4.386
Suykens, J.A.K., Vandewalle, J.P.L., De Moor, B.L.R., 1996. Artificial Neural Networks for Modelling and Control of Nonlinear Systems. Kluwer, Boston.
Szu, 1996, Wavelet transforms and neural networks for compression and recognition, Neural Networks, 9, 695, 10.1016/0893-6080(95)00051-8
Tam, 1992, Managerial applications of neural networks: The case of bank failure predictions, Management Science, 38, 926, 10.1287/mnsc.38.7.926
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 Journal on Computing, 5, 374, 10.1287/ijoc.5.4.374
Tong, 1980, Threshold autoregressive, limit cycles and cyclical data, Journal of the Royal Statistical Society Series B, 42, 245
Trippi, R.R., Turban, E., 1993. Neural Networks in Finance and Investment: Using Artificial Intelligence to Improve Real-world Performance. Probus, Chicago.
Turkkan, 1995, Prediction of wind load distribution for air-supported structures using neural networks, Canadian Journal of Civil Engineering, 22, 453, 10.1139/l95-053
Vishwakarma, K.P., 1994. A neural network to predict multiple economic time series. In: Proceedings of the IEEE International Conference on Neural Networks, 6, pp. 3674–3679.
Wallich, P., 1991. Wavelet theory: An analysis technique that's creating ripples. Scientific American, January, 34–35.
Wang, 1994, A procedure for determining the topology of multilayer feedforward neural networks, Neural Networks, 7, 291, 10.1016/0893-6080(94)90023-X
Wasserman, P.D., 1989. Neural Computing: Theory and Practice. Van Nostrand, Reinhold, New York.
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
Weigend, A.S., Gershenfeld, N.A., 1993. Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley, Reading, MA.
Weigend, 1990, Predicting the future: A connectionist approach, International Journal of Neural Systems, 1, 193, 10.1142/S0129065790000102
Weigend, A.S., Huberman, B.A., Rumelhart, D.E., 1992. Predicting sunspots and exchange rates with connectionist networks. In: Casdagli, M., Eubank, S. (Eds.), Nonlinear Modeling and Forecasting. Addison-Wesley, Redwood City, CA, pp. 395–432.
Weigend, 1991, Generalization by weight-elimination with application to forecasting, Advances in Neural Information Processing Systems, 3, 875
Werbos, P.J., 1974. Beyond regression: new tools for prediction and analysis in the behavioral sciences. Ph.D. thesis, Harvard University.
Werbos, 1988, Generalization of backpropagation with application to a recurrent gas market model, Neural Networks, 1, 339, 10.1016/0893-6080(88)90007-X
White, H., 1988. Economic prediction using neural networks: The case of IBM daily stock returns. In: Proceedings of the IEEE International Conference on Neural Networks, 2, pp. 451–458.
White, 1989, Learning in artificial neural networks: A statistical perspective, Neural Computation, 1, 425, 10.1162/neco.1989.1.4.425
Widrow, 1994, Neural networks: Applications in industry, business and science, Communications of the ACM, 37, 93, 10.1145/175247.175257
Wilson, R., Sharda, R., 1992. Neural networks. OR/MS Today, August, 36–42.
Wilson, 1994, Bankruptcy prediction using neural networks, Decision Support Systems, 11, 545, 10.1016/0167-9236(94)90024-8
Wong, 1995, A bibliography of neural networks business application research: 1988–September 1994, Expert Systems, 12, 253, 10.1111/j.1468-0394.1995.tb00114.x
Wong, 1991, Time series forecasting using backpropagation neural networks, Neurocomputing, 2, 147, 10.1016/0925-2312(91)90045-D
Wong, F.S., Wang, P.Z., Goh, T.H., Quek, B.K., 1992. Fuzzy neural systems for stock selection. Financial Analysis Journal, Jan./Feb., 47–52.
Wong, 1995, A neural network approach to stock market holding period returns, American Business Review, 13, 61
Wu, 1995, Model-free forecasting for nonlinear time series (with application to exchange rates), Computational Statistics and Data Analysis, 19, 433, 10.1016/0167-9473(94)00008-7
Yao, 1996, Evolving wavelet neural networks for function approximation, Electronics Letters, 32, 360, 10.1049/el:19960229
Yoon, Y., Swales, G., 1991. Predicting stock price performance: A neural network approach. In: Proceedings of the 24th Hawaii International Conference on System Sciences., 4, pp. 156–162.
Yu, 1995, Dynamic learning rate optimization of the backpropagation algorithm, IEEE Transactions on Neural Networks, 6, 669, 10.1109/72.377972
Zhang, 1995, Wavelet neural networks for function learning, IEEE Transactions on Signal Processing, 43, 1485, 10.1109/78.388860
Zhang, 1994, Time series analysis and prediction by neural networks, Optimization Methods and Software, 4, 151, 10.1080/10556789408805584
Zhang, X., Hutchinson, J., 1993. Simple architectures on fast machines: Practical issues in nonlinear time series prediction. In: Weigend, A.S., Gershenfeld, N.A. (Eds.), Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley, Reading, MA.