Abstract
Demand prediction plays a crucial role in advanced systems for supply chain management. Having a reliable estimation for a product’s future demand is the basis for the respective systems. Various forecasting techniques have been developed, each one with its particular advantages and disadvantages compared to other approaches. This motivated the development of hybrid systems combining different techniques and their respective advantages. Based on a comparison of ARIMA models and neural networks we propose to combine these approaches to a sequential hybrid forecasting system. In our system the output from an ARIMA-type model is used as input for a neural network which tries to reproduce the original time series. The applications on time series representing daily product sales in a supermarket underline the excellent performance of the proposed system.
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References
Abraham, A., Nath, B.: A neuro-fuzzy approach for modeling electricity demand in Victoria. Applied Soft Computing 1(2), 127–138 (2001)
Abraham, A., Philip, S., Mahanti, P.K.: Soft Computing Models for Weather Forecasting. International Journal of Applied Science and Computations 11(3), 106–117 (2004)
Aburto, L., Weber, R.: Improved Supply Chain Management based on Hybrid Demand Forecasts. Applied Soft Computing 7, 136–144 (2007)
Akaike, H., Kitagawa, G. (eds.): The Practice of Time Series Analysis. Springer, Berlin (1999)
Akaike, H.: A new look at the statistical model identification. IEEE Trans. Automatic Control AC-19, 716–723 (1974)
Bansal, K., Vadhavkar, S., Gupta, A.: Neural Networks Based Forecasting Techniques for Inventory Control Applications. Data Mining and Knowledge Discovery 2, 97–102 (1998)
Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1976)
Chen, Y., Yang, B., Dong, J., Abraham, A.: Time-series forecasting using flexible neural tree model. Information Sciences 174(3-4), 219–235 (2005)
Clemen, R.T.: Combining forecasts: A review and annotated bibliography. International Journal of Forecasting 5, 559–581 (1989)
Crone, S., Guajardo, J., Weber, R.: A study on the ability of Support Vector Regression and Neural Networks to Forecast Basic Time Series Patterns. In: Bramer, M. (ed.) IFIP International Federation for Information Processing. Artificial Intelligence in Theory and Practice, vol. 217, pp. 149–158. Springer, Boston (2006), doi: http://dx.doi.org/10.1007/978-0-387-34747-9_16
Donaldson, R.G., Kamstra, M.: Forecast combining with neural networks. Journal of Forecasting 15, 49–61 (1996)
Dorffner, G.: Neural Networks for Time Series Processing. Neural Network World 6(4), 447–468 (1996)
Faraway, J., Chatfield, C.: Time series forecasting with neural networks: a comparative study using the airline data. Applied Statistics 47(2), 231–250 (1998)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)
Hill, T., O’Connor, M., Remus, W.: Neural Networks for Time Series Forecasts. Management Science 42(7), 1082–1092 (1996)
Hornik, K.: Approximation capabilities of multilayer feedforward networks. Neural Networks 4, 251–257 (1991)
Kuo, C., Reitsch, A.: Neural networks vs. conventional methods of forecasting. The Journal of Business Forecasting Methods and Systems 14(4), 17–22 (1995)
Moody, J.: Prediction Risk and Architecture Selection for Neural Networks. In: Cherkassky, V., Friedman, J.H., Wechsler, H. (eds.) From Statistics to Neural Networks: Theory and Pattern Recognition Applications. NATO ASI Series F, Springer, Berlin (1994)
Reyes-Aldasoro, C.C., Ganguly, A., Lemus, G., Gupta, A.: A Hybrid Model Based on Dynamic Programming, Neural Networks and Surrogate Value for Inventory Optimisation Applications. Journal of Operational Research Society 50(1), 85–94 (1999)
Wan, E.A.: Finite impulse response neural networks for autoregressive time series prediction. In: Weigend, A., Gershenfeld, N. (eds.) Predicting the Future and Understanding the Past, Addison-Wesley, MA (1993)
Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)
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Aburto, L., Weber, R. (2007). A Sequential Hybrid Forecasting System for Demand Prediction. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_39
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DOI: https://doi.org/10.1007/978-3-540-73499-4_39
Publisher Name: Springer, Berlin, Heidelberg
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