Market Forecasts and Client Behavioral Data: Towards Finding Adequate Model Complexity

Open access


Using efficient marketing strategies for understanding and improving the relation between vendors and clients rests upon analyzing and forecasting a wealth of data which appear at different time resolutions and at levels of aggregation. More often than not, market success does not have consistent explanations in terms of a few independent influence factors. Indeed, it may be difficult to explain why certain products or services tend to sell well while others do not. The rather limited success of finding general explanations from which to draw specific conclusions good enough in order to generate forecasting models results in our proposal to use data driven models with no strong prior hypothesis concerning the nature of dependencies between potentially relevant variables. If the relations between the data are not purely random, then a general or flexible enough data driven model will eventually identify them. However, this may come at a high cost concerning computational resources and with the risk of overtraining. It may also preclude any useful on-line or real time applications of such models. In order to remedy this, we propose a modeling cycle which provides information about the adequacy of a model complexity class and which also highlights some nonstandard measures of expected model performance.

1. Bishop, C. M., “Neural Networks for Pattern Recognition”, Oxford University Press 1995

2. Bishop, C. M., “Pattern Recognition and Machine Learning”, Springer 2006

3. Baydin, A.G., Pearlmutter, B.A., Radul, A.A., and Siskind, J.M., “Automatic differrentiation in machine learning: a survey”, The Journal of Machine Learning Research, Vol.18, nr.153, pp1-43, 2018

4. Bevan, A., “Machine Learning with Tensorflow”, Slides of an applied lecture, (accessed at 20.5.2018)

5. Bourez, C., “Deep Learning with Theano”, Packt Publishing 2017

6. Chollet, F., “Introduction to Keras”, March 9th, 2018, Slides of an applied lecture,

7. Hinton, G.E., Osindero, S., and The, Y., “A fast learning algorithm for deep belief nets”, Neural Computation, Vol. 18, pp1527-1554, 2006

8. G. Montavon, G., Orr, G.B., and Müller K.-R., “Neural Networks: Tricks of the Trade”, Springer, 2012.

9. Gareth, J., Witten, D., Hastie, T., and Tibshirani, R., “An Introduction to Statistical Learning”, New York, Springer, 2015.

10. Harrison, D. and Rubinfeld, D.L. “Hedonic prices and the demand for clean air”, Journal of Environmental. Economics & Management, vol.5, 81-102, 1978.

11. Hirasawa, K., Wang, X., Murata, J., Hu, J., and Jin, C., “Universal learning network and its application to chaos control”, Neural Networks 13, pp239-252, 2000

12. Hosmer, D. W., and Lemeshow, S., “Applied Logistic Regression”. Wiley 2000.

13. Ian Goodfellow, I., Bengio, Y., and Courville, A., “Deep Learning”, MIT Press 2016.

14. Ioffe, S., and Szegedy, C., "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift", 2015.

15. Kaminski, B., Jakubczyk, M., and Szufel, P., "A framework for sensitivity analysis of decision trees". Central European Journal of Operations Research, 2017.

16. Moro, S., Cortez, P. and Rita, P., “A Data-Driven Approach to Predict the Success of Bank Telemarketing”. Decision Support Systems, Vol. 62, pp22-31, 2014.

17. Liou, C.-Y., Cheng, W,-C., Liou, J.-W., and Liou, D.-R., "Autoencoder for words", Neurocomputing, 139, 2014.

18. McLachlan, G. J., “Discriminant Analysis and Statistical Pattern Recognition”. Wiley Interscience, 2014

19. Srivastava, N., Hinton, G., Krizhevsky, A., and Sutskever, I., “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”. Journal of Machine Learning Research 15 1929-1958. (2014).

20. Kotsiantis, S., Kanellopoulos, D., and Pintelas, P., "Data Preprocessing for Supervised Learning", International Journal of Computer Science, Vol. 1 N. 2, pp 111-117. (2006)

21. Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P., “Numerical Recipes”, 3rd edition, Cambridge University Press 2007

22. Schebesch, K.B., “Marktprognosen und Produktdesign mit datenorientierter KI”, Habilitation, University of Bremen 2003, 688pp

23. Trippa, L., Waldron, L., and Huttenhower, C.; Parmigiani, "Bayesian nonparametric cross-study validation of prediction methods". The Annals of Applied Statistics. 9 (1): 402-428. (2015).

24. Zimmermann H.G., and Weigend, A.S. “Representing Dynamical Systems in Feedforward Networks: A Six Layer Architecture”, in: Weigend, S.A., Abu-Mostafa, y., and Refenes, A.P.N. (eds.) Decision Technologies for Financial Engineering, Neural Networks in the Capital Markets 96, pp289-306, 1997

25. Repo1, (accessed at 04.06.2018)

26. Repo2, (accessed at 06.06.2018)

27. Rcran, (R version 3.5.0 accessed at 07.05.2018).

Journal Information


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 94 94 3
PDF Downloads 52 52 1