A hybrid system-identification method for forecasting telecommunications product demands

International Journal of Forecasting

Volumn 18, Issue 4, 2002, Pages 647-671

  Cox Jr , Louis A ^a   Popken, Douglas A ^b  

^a Cox Associates ^*  (United States)

^b Systems View   (United States)

Author keywords

Classification; Combining forecasts; Market forecasting; Markov models; State space models; Telecommunications; Transition probabilities

Indexed keywords

EID: 0036815935     PISSN: 01692070     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0169-2070(02)00069-9     Document Type: Article

References (12)

1. Aoki, M. (1996). New Approaches to Macroeconomic Modeling. New York: Cambridge University Press.
2. Baum, L. E., & Petrie, T. (1966). Statistical inference for probabilistic functions of finite State Markov Chains. Ann. Math. Stat., 37, 1554-1563.
3. Bhat, U. N. (1984). Elements of Applied Stochastic Processes, 2nd ed.. New York: Wiley.
4. Biggs, D., De Ville, B., & Suen, E. (1991). A method of choosing multiway partitions for classification and decision trees. J . Appl. Stat., 18(1), 49-62.
5. Breiman, L., Olshen, R., Friedman, J., & Stone, C. (1984). Classification and Regression Trees. Wadsworth Publishing.
6. Cox, Jr. L. A. (2001). Forecasting demand for telecommunications products from cross-sectional data. Telecommun. Sys., 16(3/4), 437-454.
7. Elliott, R. J., Aggoun, L., & Moore, J. B. (1995). Hidden Markov Models : Estimation and Control. New York: Springer Verlag.
8. Fildes, R. (2002). Telecommunications demand forecasting-a review. International Journal of Forecasting, 164, 489-522.
9. Lancaster, T. (1990). The Econometric Analysis of Transition Data. New York: Cambridge University Press.
10. Ross, S. M. (1983). Stochastic Processes. New York: Wiley.
11. Schober, D. (1999). Data detectives: What makes customers tick? Telephony, 237(9), 21-24.
12. Strouse, K. A. (1999). Weapons of mass marketing. Telephony, 237(9), 26-28.