The previous version of the MAPA package implemented only the univariate aspect of the algorithm. Version 2.0.1 implements MAPAx as well, which allows incorporating regressors in your forecasts. In this paper we demonstrated the usefulness of temporal aggregation in the case of forecasting demand in the presence of promotions. In particular, we showed that MAPAx… Read More: MAPAx available for R & new MAPA package on CRAN »
Here is the link: https://github.com/trnnick/mapa It has been a long time I wanted to rework the MAPA package for R, but I could not find the time. Finally I got around starting it. There are three objectives in this: Clean up code and introduce S3methods. MAPA was the first package that I wrote! Incorporate the… Read More: MAPA package for R on GitHub »
We have been re-working the TStools package over the past couple of weeks. The major changes are: The es function that is an alternative to the ets function from the forecast package has been removed. Now it is published separately in the smooth package, which contains a collection of interesting implementations for exponential smoothing, ARIMA… Read More: TStools recent changes »
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” »
Fotios Petropoulos and I will be giving a workshop on how to produce forecasts using R. The focus will be on business forecasting applications and the workshop is part of the workshop series that runs as part of the 36th International Symposium on Forecasting on the 19th of June 2016. This workshop will provide a… Read More: Workshop on `Forecasting with R’ »
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 this post I introduce a new bias metric that has several desirable properties over traditional ones. When evaluating forecasting performance it is important to look at two elements: forecasting accuracy and bias. Although there has been substantial progress in the measurement of accuracy with various metrics being proposed, there has been rather limited progress… Read More: The Bias Coefficient: a new metric for forecast bias »
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 »
Shiny is a web application framework for R. The idea is simple: deploy R code in webpages. This might prove useful when user interaction is required, for instance to design and deploy forecasting experiments that need human participants. I gave it a try to see how easy is it to build a demo. Assuming your… Read More: Experimenting with Shiny for R »