References
- Brown, R.G. (1959). Statistical Forecasting for Inventory control, McGraw-Hill: New York, NY.
- Brown, R.G. (1962). Smoothing, Forecasting and Prediction of Discrete Time Series, Prentice Hall: New Jersey, NJ.
- De Gooijer, J.G., Hyndman, R.J. (2006). 25 years of Time Series Forecasting. International Journal of Forecasting, Vol. 22, No. 3, pp. 443–473. doi: 10.1016/j.ijforecast.2006.01.001
- Fomby, T.B. (2008). Exponential Smoothing Models. Class Notes Version 6. Department of Economics. Southern Methodist University, Dallas, TX, June 2008.
- Gardner, E. S., & McKenzie, E. (1985). Forecasting trends in time series. Management Science, 31(10), 1237–1246. https://doi.org/10.1287/mnsc.31.10.1237
- Harvey, A.C. (1989). Forecasting, Structural Time Series Models, and the Kalman Filter. Cambridge University Press.
- Holt, C. E. (1957). Forecasting seasonals and trends by exponentially weighted averages (O.N.R. Memorandum No. 52). Carnegie Institute of Technology, Pittsburgh, USA. https://doi.org/10.1016/j.ijfore-cast.2003.09.015
- Hyndman, R.J., and Athanasopoulos, G. (2018). Forecasting Principles and Practices. Monash University, Australia. https://otexts.org/fpp2/
- Levenberg, Kenneth (1944). A Method for the Solution of Certain Non-Linear Problems in Least Squares. Quarterly of Applied Mathematics, Vol. 2, pp. 164–168. doi: 10.1090/qam/10666
- Marquardt, Donald (1963). An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM Journal on Applied Mathematics, Vol. 11, No. 2, pp. 431–441. doi:10.1137/0111030
- Rahardja, D. (2005). X-Charts versus X/MR Chart Combinations: IID Cases & Non-IID Cases. Quality Engineering, Vol. 17, No. 2, pp. 189– 196. doi: 10.1081/QEN-200057318
- Rahardja, D., Yang, Y., and Zhang, Z. (2016). A Comprehensive Review of the Two-Sample Independent or Paired Binary Data – with or without Stratum Effects. Journal of Modern Applied Statistical Methods, Vol. 15, No. 2, pp. 215–223. doi: 10.22237/jmasm/1478002440
- Rahardja, D. (2017). A Review of the Multiple-Sample Tests for the Continuous-Data Type. Journal of Modern Applied Statistical Methods, Vol. 16, No. 1, pp. 127–136. doi: 10.22237/jmasm/1493597220
- Rahardja, D., and Wu, H. (2018). Statistical methodological review for time-to-event data. Journal of Statistics and Management Systems, Vol. 21, No. 1, pp. 189–199. doi: 10.1080/09720510.2017.1411029
- SAS Institute Inc. (1995). SAS Manual SAS/ETS Software: Time Series Forecasting System, Version 6, First Edition, pp. 225–235. Cary, NC: SAS Institute Inc.
- SAS Institute Inc. (2013). Base SAS® 9.4 Procedures Guide: Statistical Procedures, Second Edition. Cary, NC: SAS Institute Inc.
- SAS Institute Inc. (2015). SAS® Forecast Studio 14.1: User’s Guide. Cary, NC: SAS Institute Inc.
- SAS Institute Inc. (2017). SAS/ETS® 14.3 User’s Guide. Cary, NC: SAS Institute Inc.
- Sypsas, P.T. (1989). Identifying Patterns in Multiple Time Series Data. Journal of Information and Optimization Sciences Vol.10, No. 3, pp. 471–494. doi: 10.1080/02522667.1989.10698981
- Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6, 324–342. https://doi.org/10.1287/mnsc.6.3.324
- Zhou, M. (2017). Consistency of the Kaplan-Meier Estimator when Data are Independent Non-Identically Distributed. Technical Report, Department of Statistics, University of Kentucky, Lexington, KY 40506-0027, file property created 11 September 2017, pp. 1–15. www.ms.uky.edu/~mai/sta709/Tech351-1.pdf