I am delighted to receive the news that my recent paper with George Athanasopoulos at the European Journal of Operational Research has been selected as the EJOR editor’s choice article for June 2020. My thanks to the editor and the reviewers for their help with their comments and recommendations in improving the paper and bringing… Read More: Intermittent demand & THieF – EJOR Editors’ Choice Articles »
Nikolaos Kourentzes and George Athanasopoulos, 2020. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2020.05.046
N. Kourentzes, D. Li and A.K. Strauss, 2017. Journal of Revenue & Pricing Management.
F. Petropoulos, N. Kourentzes and K. Nikolopoulos, 2016, International Journal of Production Economics, 181: 154-161. http://dx.doi.org/10.1016/j.ijpe.2016.04.017
Together with Fotios Petropoulos we gave a workshop on producing forecasts with R, at the International Symposium on Forecasting, 2016. You can find the material of the workshop here. The workshop notes assume knowledge of what the various forecasting methods do, which are only briefly explained in the workshop’s slides, and mostly focuses in showing… Read More: Material for `Forecasting with R: A practical workshop” »
Version 1.8 of tsintermittent has been submitted to CRAN and should be shortly available for download. Amongst various new checks on inputs to better accommodate handling multiple time series with data frames, a new option has been added to data.frc. When method=”auto” two things will happen: Function idclass(…,type=”PKa”) will be called to classify the time… Read More: Another update for tsintermittent »
I uploaded a new version (1.7) of tsintermittent on CRAN. Apart from fixing a couple of minor issues, a new function has been added to help scaling up forecasting. Recently I had a few requests to add a functionality to use data frames with multiple time series as inputs. I have included a new wrapper… Read More: Forecasting multiple time series with tsintermittent »
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: Exponential smoothing demo »
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: Choosing parameters for Croston’s method and its variants »
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: Update for the tsintermittent package »