Fitting the damped trend method of exponential smoothing

References

• Armstrong, J. S. (2006). Findings from evidence-based forecasting: Methods for reducing forecast error. International Journal of Forecasting, 22(3), 583–598.10.1016/j.ijforecast.2006.04.006
   Web of Science ®Google Scholar
• Armstrong, J. S., Green, K. C., & Graefe, A. (2015). Golden rule of forecasting: Be conservative. Journal of Business Research, 68(8), 1717–1731.10.1016/j.jbusres.2015.03.031
   Web of Science ®Google Scholar
• Brown, R. G. (1963). Smoothing. forecasting and prediction of discrete time series. Englewood Cliffs, NJ: Prentice-Hall.
   Google Scholar
• Cogger, K. O. (1979). Time series analysis and forecasting with an absolute error criterion. TIMS Studies in the Management Sciences, 12, 189–201.
   Google Scholar
• Collopy, F., & Armstrong, J. S. (1992). Rule-based forecasting: Development and validation of an expert systems approach to combining time series extrapolations. Management Science, 38(10), 1394–1414.10.1287/mnsc.38.10.1394
   Web of Science ®Google Scholar
• Davydenko, A., & Fildes, R. (2016). Forecast error measures: Critical review and practical recommendations. In M. Gilliland, U. Sglavo, & L. Tashman (Eds.), Business forecasting: Practical problems and solutions (pp. 238–249). New York, NY: John Wiley.
   Google Scholar
• Fildes, R., & Makridakis, S. (1988). Forecasting and loss functions. International Journal of Forecasting, 4(4), 545–550.10.1016/0169-2070(88)90131-8
   Web of Science ®Google Scholar
• Fildes, R., Hibon, M., Makridakis, S., & Meade, N. (1998). Generalizing about univariate forecasting methods: Further empirical evidence. International Journal of Forecasting, 14(3), 339–358.10.1016/S0169-2070(98)00009-0
   Web of Science ®Google Scholar
• Fildes, R., Nikolopoulos, K., Crone, S.F., & Syntetos, A.A. (2008). Forecasting and operational research: A review. Journal of the Operational Research Society, 59(9), 1150–1172.10.1057/palgrave.jors.2602597
   Web of Science ®Google Scholar
• Fylstra, D., Lasdon, L., Watson, J., & Waren, A. (1998). Design and use of the microsoft excel solver. Interfaces, 28(5), 29–55.10.1287/inte.28.5.29
   Web of Science ®Google Scholar
• Gardner Jr, E. S., & Mckenzie, E. (1985). Forecasting trends in time series. Management Science, 31(10), 1237–1246.10.1287/mnsc.31.10.1237
   Web of Science ®Google Scholar
• Gardner Jr, E. S. (1999). Rule-based forecasting vs. damped-trend exponential smoothing. Management Science, 45(8), 1169–1176.10.1287/mnsc.45.8.1169
   Web of Science ®Google Scholar
• Gardner Jr, E. S. (2006). Exponential smoothing: The state of the art – Part II. International Journal of Forecasting, 22(4), 637–666.10.1016/j.ijforecast.2006.03.005
   Web of Science ®Google Scholar
• Gardner Jr, E. S. (2015). Conservative forecasting with the damped trend. Journal of Business Research, 68(8), 1739–1741.10.1016/j.jbusres.2015.03.033
   Web of Science ®Google Scholar
• Gardner Jr, E. S., & Diaz-Saiz, J. (2008). Exponential smoothing in the telecommunications data. International Journal of Forecasting, 24(1), 170–174.10.1016/j.ijforecast.2007.05.002
   Web of Science ®Google Scholar
• Gardner Jr, E. S., & McKenzie, E. (2011). Why the damped trend works. Journal of the Operational Research Society, 62(6), 1177–1180.10.1057/jors.2010.37
   Web of Science ®Google Scholar
• Goodwin, P., & Lawton, R. (1999). On the asymmetry of the symmetric MAPE. International Journal of Forecasting, 15(4), 405–408.10.1016/S0169-2070(99)00007-2
   Web of Science ®Google Scholar
• Green, K. C., & Armstrong, J. S. (2015). Simple versus complex forecasting: The evidence. Journal of Business Research, 68(8), 1678–1685.10.1016/j.jbusres.2015.03.026
   Web of Science ®Google Scholar
• Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5–10.10.1016/j.ijforecast.2003.09.015
   Web of Science ®Google Scholar
• Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3), 439–454.10.1016/S0169-2070(01)00110-8
   Web of Science ®Google Scholar
• Hyndman, R. J., & Billah, B. (2003). Unmasking the theta method. International Journal of Forecasting, 19(2), 287–290.10.1016/S0169-2070(01)00143-1
   Web of Science ®Google Scholar
• Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.10.1016/j.ijforecast.2006.03.001
   Web of Science ®Google Scholar
• Hyndman, R., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: The state space approach. Berlin: Springer-Verlag.10.1007/978-3-540-71918-2
   Google Scholar
• Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., … Winkler, R. (1982). The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting, 1(2), 111–153.10.1002/(ISSN)1099-131X
   Web of Science ®Google Scholar
• Makridakis, S., & Hibon, M. (1991). Exponential smoothing: The effect of initial values and loss functions on post-sample forecasting accuracy. International Journal of Forecasting, 7(3), 317–330.10.1016/0169-2070(91)90005-G
   Web of Science ®Google Scholar
• Makridakis, S., & Hibon, M. (2000). The M3-competition: Results, conclusions and implications. International Journal of Forecasting, 16(4), 451–476.10.1016/S0169-2070(00)00057-1
   Web of Science ®Google Scholar
• McKenzie, E., & Gardner Jr, E. S. (2010). Damped trend exponential smoothing: A modelling viewpoint. International Journal of Forecasting, 26(4), 661–665.10.1016/j.ijforecast.2009.07.001
   Web of Science ®Google Scholar
• NCSS Statistical Software. (2017). Exponential smoothing – Trend and seasonal (Chapter 467). Retrieved 2017, August 1 from https://www.ncss.com,
   Google Scholar
• Snyder, R. D., & Koehler, A. B. (2009). Incorporating a tracking signal into a state space model. International Journal of Forecasting, 25(3), 526–530.10.1016/j.ijforecast.2008.12.003
   Web of Science ®Google Scholar
• Vokurka, R. J., Flores, B. E., & Pearce, S. L. (1996). Automatic feature identification and graphical support in rule-based forecasting: A comparison. International Journal of Forecasting, 12(4), 495–512.10.1016/S0169-2070(96)00682-6
   Web of Science ®Google Scholar
• Wagner, H. M. (1959). Linear programming techniques for regression analysis. Journal of the American Statistical Association, 54(285), 206–212.10.1080/01621459.1959.10501506
   Web of Science ®Google Scholar
• Weiss, A. A., & Andersen, A. P. (1984). Estimating time series models using the relevant forecast evaluation criterion. Journal of the Royal Statistical Society. Series A (General), 484–487.10.2307/2981579
   Web of Science ®Google Scholar
• Zellner, A. (1986). A tale of forecasting 1001 series: The Bayesian Knight strikes again. International Journal of Forecasting, 2(4), 491–494.10.1016/0169-2070(86)90094-4
   Web of Science ®Google Scholar