Spodaj je seznam nekatere priporočene dodatne literature za seminar prof. dr. Liljane Ferbar Tratar o metodah Holt-Wintersa (gradiva dobite s klikom na naslov).
Babai, M.Z., Ali, M.M., Boylan, J.E., Syntetos, A.A., 2013. Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis. International Journal of Production Economics, 143, 463–471.
Billah, B., King, M.L., Snyder, R.D., Koehler, A.B., 2006. Exponential smoothing model selection for forecasting. International Journal of Forecasting, 22, 239-247.
Croston, J.D., 1972. Forecasting and stock control for intermittent demands. Operational Research Quarterly, 23, 289-304.
De Livera, A.M., Hyndman, R.J., Snyder, R.D., 2011. Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106, 1513-1527.
Fildes, R.A., Nikolopous, K., Crone, S.F., Syntetos, A.A., 2008. Forecasting and operational research: a review. Journal of the Operational Research Society 59, 1150–1172.
Fildes, R.A., Petropoulos, F. 2013. An evaluation of simple forecasting mode selection rules. MPRA Paper No. 51772.
Gardner Jr., E.S., 1985. Exponential smoothing: The state of the art. Journal of Forecasting, 4, 1-28.
Gardner Jr., E.S., 2006. Exponential smoothing: The state of the art—Part II. International Journal of Forecasting, 22, 637-666.
Gardner Jr., E.S., Dannenbring, G.D., 1980. Forecasting with exponential smoothing: Some guidelines for model selection. Decision Science, 11, 370-383.
Gardner Jr., E.S., McKenzie, E., 1985. Forecasting trends in time series. Management Science, 31, 1237-1246.
Gardner Jr., E.S., McKenzie, E., 2010. Damped trend exponential smoothing: A modelling viewpoint. International Journal of Forecasting, 26, 661–665.
Gardner Jr., E.S., McKenzie, E., 2011. Why the damped trend works. Journal of the Operational Research Society 62, 1177-1180.
Gorr, W.L., Schneider, M.J., 2013. Large-change forecast accuracy: Reanalysis of M3-Competition data using receiver operating characteristic analysis. International Journal of Forecasting, 29, 274-281.
Groff, G.K., 1973. Empirical comparison of models for short range forecasting. Management Science, 20, 22-31.
Holt, C.C., 1957. Forecasting seasonals and trends by exponentially weighted moving averages. Office of Naval Research, Research Memorandum 52.
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, 439-454.
Lawton, R. 1998. How should additive Holt–Winters estimates be corrected? International Journal of Forecasting, 14, 393-403.
Makridakis, S., Hibon, M., 2000. The M3-Competition: results, conclusions and implications. International Journal of Forecasting, 16, 451-476.
Petropoulos, F., Makridakis, S., Assimakopoulos, V., Nikolopoulos, K., 2014. 'Horses for Courses’ in demand forecasting. European Journal of Operational Research, 237, 152-163.
Rasmussen, R., 2004. On time series data and optimal parameters. Omega, The International Journal of Management Science, 32, 111-120.
Snyder, R., 2002. Forecasting sales of slow and fast moving inventories. European Journal of Operational Research, 140, 684–699.
Strijbosch, L.W.G., Syntetos, A.A., Boylan, J.E., Janssen, E., 2011. On the interaction between forecasting and stock control: The case of non-stationary demand. International Journal of Production Economics, 133, 470–480.
Syntetos, A.A , Boylan, J.E., 2001. On the bias of intermittent demand estimates. International Journal of Production Economics, 71, 457-466.
Taylor, J.W., 2003a. Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting, 19, 715–725.
Taylor, J.W., 2003b. Short-term electricity demand forecasting using double seasonal exponential smoothing. The Journal of the Operational Research Society, 54, 799-805.
Wallström, P., Segerstedt, A., 2010. Evaluation of forecasting error measurements and techniques for intermittent demand. International Journal of Production Economics, 128, 625-636.
Winters, P.R., 1960. Forecasting sales by exponentially weighted moving averages. Management Science, 6, 324-342.
Zhang, G.P., Kline, D.M., 2007. Quarterly Time-Series Forecasting With Neural Networks. IEEE Transactions on Neural Networks, 18, 1800-1814.
Zhao, X., Lee, T.S., 1993. Freezing the master production schedule in multilevel material requirements planning systems under demand uncertainty. Journal of Operations Management, 11, 185-205.
Babai, M.Z., Ali, M.M., Boylan, J.E., Syntetos, A.A., 2013. Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis. International Journal of Production Economics, 143, 463–471.
Billah, B., King, M.L., Snyder, R.D., Koehler, A.B., 2006. Exponential smoothing model selection for forecasting. International Journal of Forecasting, 22, 239-247.
Croston, J.D., 1972. Forecasting and stock control for intermittent demands. Operational Research Quarterly, 23, 289-304.
De Livera, A.M., Hyndman, R.J., Snyder, R.D., 2011. Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106, 1513-1527.
Fildes, R.A., Nikolopous, K., Crone, S.F., Syntetos, A.A., 2008. Forecasting and operational research: a review. Journal of the Operational Research Society 59, 1150–1172.
Fildes, R.A., Petropoulos, F. 2013. An evaluation of simple forecasting mode selection rules. MPRA Paper No. 51772.
Gardner Jr., E.S., 1985. Exponential smoothing: The state of the art. Journal of Forecasting, 4, 1-28.
Gardner Jr., E.S., 2006. Exponential smoothing: The state of the art—Part II. International Journal of Forecasting, 22, 637-666.
Gardner Jr., E.S., Dannenbring, G.D., 1980. Forecasting with exponential smoothing: Some guidelines for model selection. Decision Science, 11, 370-383.
Gardner Jr., E.S., McKenzie, E., 1985. Forecasting trends in time series. Management Science, 31, 1237-1246.
Gardner Jr., E.S., McKenzie, E., 2010. Damped trend exponential smoothing: A modelling viewpoint. International Journal of Forecasting, 26, 661–665.
Gardner Jr., E.S., McKenzie, E., 2011. Why the damped trend works. Journal of the Operational Research Society 62, 1177-1180.
Gorr, W.L., Schneider, M.J., 2013. Large-change forecast accuracy: Reanalysis of M3-Competition data using receiver operating characteristic analysis. International Journal of Forecasting, 29, 274-281.
Groff, G.K., 1973. Empirical comparison of models for short range forecasting. Management Science, 20, 22-31.
Holt, C.C., 1957. Forecasting seasonals and trends by exponentially weighted moving averages. Office of Naval Research, Research Memorandum 52.
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, 439-454.
Lawton, R. 1998. How should additive Holt–Winters estimates be corrected? International Journal of Forecasting, 14, 393-403.
Makridakis, S., Hibon, M., 2000. The M3-Competition: results, conclusions and implications. International Journal of Forecasting, 16, 451-476.
Petropoulos, F., Makridakis, S., Assimakopoulos, V., Nikolopoulos, K., 2014. 'Horses for Courses’ in demand forecasting. European Journal of Operational Research, 237, 152-163.
Rasmussen, R., 2004. On time series data and optimal parameters. Omega, The International Journal of Management Science, 32, 111-120.
Snyder, R., 2002. Forecasting sales of slow and fast moving inventories. European Journal of Operational Research, 140, 684–699.
Strijbosch, L.W.G., Syntetos, A.A., Boylan, J.E., Janssen, E., 2011. On the interaction between forecasting and stock control: The case of non-stationary demand. International Journal of Production Economics, 133, 470–480.
Syntetos, A.A , Boylan, J.E., 2001. On the bias of intermittent demand estimates. International Journal of Production Economics, 71, 457-466.
Taylor, J.W., 2003a. Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting, 19, 715–725.
Taylor, J.W., 2003b. Short-term electricity demand forecasting using double seasonal exponential smoothing. The Journal of the Operational Research Society, 54, 799-805.
Wallström, P., Segerstedt, A., 2010. Evaluation of forecasting error measurements and techniques for intermittent demand. International Journal of Production Economics, 128, 625-636.
Winters, P.R., 1960. Forecasting sales by exponentially weighted moving averages. Management Science, 6, 324-342.
Zhang, G.P., Kline, D.M., 2007. Quarterly Time-Series Forecasting With Neural Networks. IEEE Transactions on Neural Networks, 18, 1800-1814.
Zhao, X., Lee, T.S., 1993. Freezing the master production schedule in multilevel material requirements planning systems under demand uncertainty. Journal of Operations Management, 11, 185-205.
Spodaj je še nekaj monografij s področja, s povezavami do polnih besedil.
Armstrong, J.S. (ed., 2002). Principles of Forecasting: A Handbook for Researchers and Practitioners. New York, Boston, Dordrecht, London, Moscow: Kluwer Academic Publishers.
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.
Hyndman, R.J. Forecasting With R. Melbourne: Monash University.
Armstrong, J.S. (ed., 2002). Principles of Forecasting: A Handbook for Researchers and Practitioners. New York, Boston, Dordrecht, London, Moscow: Kluwer Academic Publishers.
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.
Hyndman, R.J. Forecasting With R. Melbourne: Monash University.