J.R. Trapero, N. Kourentzes, A. Martin, 2015, 27th European Conference on operational Research, Glasgow.
I. Svetunkov, N. Kourentzes, 2015, 27th European Conference on operational Research, Glasgow.
The International Symposium on Forecasting (ISF 2015) was held this week in Riverside, CA. It was a very interesting conference, with stimulating talks and a wide variety of forecasting related topics, both for academics and practitioners. It is a highly recommended conference to attend. I organised the invited session on the topic of “Forecasting with… Read More »
From time to time people have asked me how to implement Holt Winters (trend-seasonal exponential smoothing) in Excel. I have my reservations for using Excel to do your day-to-day forecasting. Nonetheless, you can find an example here.
J.R. Trapero, N. Kourentzes and A. Martin, 2015, Energy, 84: 289-295. http://dx.doi.org/10.1016/j.energy.2015.02.100
This is a guest blog entry by Fotios Petropoulos. A few months ago, Bergmeir, Hyndman and Benitez made available a very interesting working paper titled “Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation”. In short, they successfully employed the bootstrap aggregation technique for improving the performance of exponential smoothing. The bootstrap technique is… Read More »
In my experience users of exponential smoothing have often limited transparency in how the various smoothing parameters interact. I built this small demo to illustrate how the different smoothing parameters and exponential smoothing components interact. You can choose between some simulated and some real time series, as well as the option to add outliers or… Read More »
Over the years I have reviewed numerous papers that do not properly benchmark the various methods proposed. In my opinion if a paper has an empirical evaluation, then it must have appropriate benchmarks as well. Otherwise, one cannot claim that convincing empirical evidence is provided. The argument is simple: if the proposed method does not… Read More »
In most business forecasting applications, the problem usually directs the sampling frequency of the data that we collect and use for forecasting. Conventional approaches try to extract information from the historical observations to build a forecasting model. In this article, we explore how transforming the data through temporal aggregation allows us to gather additional information… 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