ATM NAKİT İKMAL OPTİMİZASYONUNDA ASİMETRİK DESTEK VEKTÖR REGRESYON TAHMİN MODELİ YAKLAŞIMI

Öz

Bankacılık ve finans sektöründe ATM nakit ikmal problemi oldukça önemlidir. Bu problemin çözümü için en düşük tahmin hata oranını veren tahmin modelinin seçilmesinin yanı sıra minimum ikmal maliyetlerini veren optimizasyon modelinin bulunması da büyük bir öneme sahiptir. Bu çalışmada, yeni bir asimetrik tahmin modeli ve bu model ile entegre olarak çalışan, bir başka deyişle, tahmin ve optimizasyondan oluşan, iki aşamalı süreci tek bir aşamaya indiren ve nakit ikmal maliyetlerini minimize eden bir optimizasyon modeli önerilmiştir. Aynı zamanda diğer tahmin modelleri ile maliyet performans karşılaştırılması gerçekleştirilmiştir.

Anahtar Kelimeler

• ATM Nakit Tahmini
• Nakit Optimizasyonu
• Destek Vektör Regresyon
• Asimetrik Destek Vektör Regresyon

Asymmetric Support Vector Regression Approach in ATM Cash Replenishment Optimization

Öz

ATM cash replenishment problem is quite important in banking and finance sector. As well as choosing the forecast model giving the smallest forecast error ratio for the solution of this problem, finding the optimization model giving the minimum replenishment costs has importance. In this study, a new asymmetrical forecast model and an optimization model running integrated with the forecast model, in other words, an optimization model which reduces the two stage forecast and optimization process to a single step is proposed. At the same time, a comparison of costs with the other forecast models is performed.

Anahtar Kelimeler

• ATM Cash Forecast
• Cash Optimization
• Support Vector Regression
• Asymmetric Support Vector Regression

Kaynakça

1. Al-Saggaf, U.M., Moinuddin, M., Arif, M., Zerguine, A., 2015, “The q-Least Mean Squares Algorithm”, Signal Processing, Cilt 111, 50-60.
2. Andrawis, R.R., Atiya, A.F., El-Shishiny, H., 2011, “Forecast combinations of computational intelligence
3. and linear models for the NN5 time series forecasting competition”, International Journal of Forecasting, Cilt 27, 672-688.
4. Armenise, R., Birtolo, C., Sangianantoni, E., Troiano, L., 2010, “A generative solution for ATM Cash Management”, 2010 International Conference of Soft Computing and Pattern Recognition IEEE, 349-356.
5. Brentnall, A.R., Crowder, M.J., Hand, D.J., 2010, “Predictive-sequential forecasting system development for cash machine stocking”, International Journal of Forecasting, Cilt 26, 764-776.
6. Castro, J., 2009, “A stochastic programming approach to cash management in banking”, European Journal of Operational Research, Cilt 192, 963-974.
7. Darwish, S.M., 2013, “A methodology to improve cash demand forecasting for ATM Network”, International Journal of Computer and Electrical Engineering, Cilt 5, 405-409.
8. Ekinci, Y., Lu, J.-C., Duman, E., 2015, “Optimization of ATM cash replenishment with group-demand forecasts”, Expert Systems with Applications, Cilt 42, 3480-3490.

9. Erdal, H.İ., 2011, “Destek Vektör Makineleri ile Tahmine Dayalı Modelleme ve Bir Uygulama”, Doktora Tezi, İstanbul Üniversitesi, Fen Bilimleri Enstitüsü.
10. Friedman, J. H., 1991, “Multivariate adaptive regression splines”, The Annals of Statistics, Cilt 19, Sayı 1,1-67.
11. Hsiao, C.-C., Su, S.-F., Chuang, C.-C., 2011, “A Rough-based Robust Support Vector Regression Network
12. for Function Approximation” 2011 IEEE International Conference on Fuzzy Systems,Taipei, Taiwan, 2814-2818, 27-30 Haziran.
13. Huang, W., Shen, L., 2008, “Weighted support vector regression algorithm based on data description”, 2008 ISECS International Colloquium on Computing, Communication, Control and Management, IEEE, 250-254.
14. Huang, X., Shi, L., Pelckmans, K., Suykens, J. A. K., 2014, “Asymmetric v-tube support vector regression”, Computational Statistics and Data Analysis, Cilt 77, 371-382.
15. Li, Z., Li, Y., Yu, F., Ge, D., 2014, “Adaptively Weighted Support Vector Regression for Financial Time Series Prediction”, 2014 International Joint Conference on Neural Networks (IJCNN), Pekin, Çin, 3062-3065, July 6-11.
16. Liu, J., Seraoui, R., Vitelli, V., Zio, E., 2013, “Nuclear power plant components condition monitoring by probabilistic support vector machine”, Annals of Nuclear Energy, Cilt 56, 23-33.
17. Lu, C.-J., 2014, “Sales forecasting of computer products based on variable selection scheme and support vector regression”, Neurocomputing, Cilt 128, 491-499.
18. Mei, L., Zhang, S., 2008, “A new weighted support vector machine for regression and its parameters
19. optimization”, Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, Cilt 5227, Editörler: Huang, D.-S., Wunsch II, D. C., Levine, D. S., Jo, K.- H., Springer Berlin Heidelberg, 597-604.
20. Nelder J.A., Mead R., 1965, “A simplex method for function minimization”, Computer Journal., Cilt 7, Sayı 4.
21. Ngo, T.-T., Huang, J.-H., Wang, C.-C., 2015, “The BFGS Method for estimating the interface temperature and convection coefficient in ultrasonic welding”, International Communications in Heat and Mass Transfer, Cilt 69, 66-75.
22. Orm{ndi, R., 2008, “Variance Minimization Least Squares Support Vector Machines for Time Series Analysis”, Data Mining, ICDM’08, Eight IEEE International Conference, 965-970.
23. Rajan, A., Malakar, T., 2015, “Optimal reactive power dispatch using hybrid Nelder-Mead simplex based firefly algorithm”, International Journal of Electrical Power and Energy Systems, Cilt 66, 9-24.
24. Sayed, A.H., 2003, “Fundamentals of Adaptive Filtering”, Wiley-Interscience, New York. Simutis, R., Dilijonas, D., Bastina, L., 2008, “Cash demand forecasting for ATM using neural networks
25. and support vector regression algorithms”, In 20th EURO mini conference – continuous optimization and knowledge-based technologies, Neringa, LITHUANIA, 416–421 , 20–23 Mayıs.
26. Stockman, M., Awad, M., Khanna, R., 2011, “Asymmetrical and Lower Bounded Support Vector Regression for Power Estimation”, Energy Aware Computing (ICEAC), 2011 International Conference, 1-6, 30 Kasım-2 Aralık.
27. Stockman, M., El Ramli, R. S., Awad, M, Jabr, R., 2012, “An Asymmetrical and Quadratic Support Vector
28. Regression Loss Function for Beirut Short Term Load Forecast”, Systems Man and Cybernetics (SMC), 2012 IEEE International Conference, 651-656, 14-17 Ekim.
29. Taieb, S.B., Bontempi, G., Atiya, A.F., Sorjamaa, A., 2012, “A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition”, Expert Systems with Applications, Cilt 39,7067-7083.
30. Teddy, S.D., Ng, S.K., 2011, “Forecasting ATM Cash Demands using a local learning model of cerebellar associative memory network”, International Journal of Forecasting, Cilt 27,760-776.
31. Teo, C.H., Vishwanathan, S.V.N., Smola, A., Le, Q.V., 2010, “Bundle Methods for Regularized Risk Minimization”, Journal of Machine Learning Research, Cilt 11, 311-365.
32. Tiryaki, S., Özşahin, Ş., Yıldırım, İ., 2014, “Comparison of artificial neural network and multiple linear regression models to predict optimum bonding strength of heat treated woods”, International Journal of Adhesion & Adhesives, Cilt 55, 29-36.
33. Vapnik, V., 1999, The Nature of Statistical Learning Theory, Springer. Vapnik, V., 2000, The Nature of Statistical Learning Theory, Springer, New York.
34. Venkatesh, K., Ravi, V., Prinzie, A., Van den Poel, D., 2014, “Cash demand forecasting in ATMs by clustering and neural networks”, European Journal of Operational Research, Cilt 232, 383-392.
35. Wang, Y., Lee, T.-H., 2014, “Asymmetric loss in the Greenbook and the Survey of Professional
36. Forecasters”, International Journal of Forecasting, Cilt 30, 235-245. Wichard, J.D., 2011, “Forecasting the NN5 time series with hybrid models”, International Journal of Forecasting, Cilt 27, 700-707.
37. Zhang, Y.M., Qi, W.G., 2009, “Interval forecasting for heating load using support vector regression and error correcting Markov Chains”, Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 1106-1110.