Extrapolative forecasting, using models such as exponential smoothing, is arguably not very complicated from a mathematical point of view, but it requires a shift in logic in terms of what is a good forecast. For this discussion I will use a simple form of exponential smoothing to demontrate my point. 1. The forecasting model: single… Read More »
Another interactive demo I created for the courses I teach. This one is about simple exponential smoothing and the main objective is to show the interaction between smoothing parameter and initial level in the fitting and holdout samples. A different interactive demo about exponential smoothing can be found here. A couple of points that may… Read More »
This is a talk I am giving today at Tasmanian School of Business and Economics at University of Tasmania. It connects two different research areas I am currently working on: solar irradiance forecasting and parameter optimisation under model uncertainty. You can find the presentation here. Half of the presentation is based on this paper if… Read More »
This is a talk that I gave at Monash University, where I am currently visiting. The topic of this research is exploring ways to avoid the assumption that the postulated model we are using is true for the data generating process of the time series we want to forecast. From this starting point we proceed… Read More »
N. Kourentzes, J.R. Trapero, 2015, 27th European Conference on operational Research, Glasgow.
I. Svetunkov, N. Kourentzes, 2015, 27th European Conference on operational Research, Glasgow.
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 »
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 »
I uploaded today to CRAN an updated version of tsintermittent. Yesterday I found a bug in the optimisation of the Croston’s, SBA and TSB methods and this update fixes that. Here is the changelog for this update: Version 1.3 (04 August 2014) * Corrected major optimisation bug – issue with opt.on in all crost, tsb… 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 »