Seasonality is a common characteristic of time series. It can appear in two forms: additive and multiplicative. In the former case the amplitude of the seasonal variation is independent of the level, whereas in the latter it is connected. The following figure highlights this: Note that in the example of multiplicative seasonality the season is… Read More »
Although Croston’s method and its variants are popular for intermittent demand time series, there have been limited advances in identifying how to select appropriate smoothing parameters and initial values. From the one hand this complicates forecasting for organisations, and from the other hand it does not permit automation. Recent research investigated various cost functions for… Read More »
Recently I posted about a paper I co-authored with Fotios Petropoulos, now in JORS: Forecast Combinations for Intermittent Demand. There we found that for intermittent demand data using multiple levels of temporal aggregation, forecasting them with the appropriate models and finally combining the forecasts performed best. This approach has many analogies with the MAPA algorithm… Read More »
N. Kourentzes, F. Petropoulos and J. R. Trapero, 2014, International Journal of Forecasting, 30: 291-302. http://dx.doi.org/10.1016/j.ijforecast.2013.09.006
J. R. Trapero, N. Kourentzes and R. Fildes, 2014, Journal of the Operantional Research Society, 66: 299-307. http://dx.doi.org/10.1057/jors.2013.174
N. Kourentzes, 2012, International Joint Conference on Neural Networks, Brisbane, 10-15 June 2012.
S. F. Crone and N. Kourentzes, 2010, Neurocomputing, 73: 1923-1936. http://dx.doi.org/10.1016/j.neucom.2010.01.017
N. Kourentzes and S. F. Crone, 2010, International Joint Conference on Neural Networks, Barcelona Spain, 18-23 July 2010.
N. Kourentzes and S. F. Crone, 2009, The 29th Annual international Symposium on Forecasting, Hong Kong.
S. F. Crone and N. Kourentzes, 2009, IJCNN’09, Atlanta, USA, IEEE: New York, pp. 3221-3228.