Choosing the most appropriate forecasting method for your time series is not a trivial task and even though there has been scientific forecasting for so many decades, how to best do it is still an open research question. Nonetheless, there are some reasonable ways to deal with the problem, which although they may not be… Read More: How to choose a forecast for your time series »
F. Petropoulos, N. Kourentzes, K. Nikolopoulos, 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: ISF 2015 and invited session on “Forecasting with Combinations and… »
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: Additive and multiplicative seasonality – can you identify them correctly? »
N. Kourentzes, 2014, International Journal of Production Economics, 156: 180-190. http://dx.doi.org/10.1016/j.ijpe.2014.06.007
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: Improving your forecasts using multiple temporal aggregation »
F. Petropoulos and N. Kourentzes, 2015, Journal of Operational Research Society, 66: 914-924. http://dx.doi.org/10.1057/jors.2014.62
We are inviting you to participate in a web-based judgmental forecasting exercise. You are asked to select the best model, based on your judgment, for 32 time series. The exercise consists of four rounds. Each round will contain 8 series and will be followed by a short questionnaire, while different types of information will be provided on top… Read More: Participate in our Judgemental Model Selection Experiment! »