Good news everyone! The future of statistical forecasting is finally here :). Have you ever struggled with ETS and needed explanatory variables? Have you ever needed to unite ARIMA and ETS? Have you ever needed to deal with all those zeroes in the data? What about the data with multiple seasonalities? All of this and […]
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Accuracy of forecasting methods: Can you tell the difference?
Previously we discussed how to measure accuracy of point forecasts and performance of prediction intervals in different cases. Now we look into the question how to tell the difference between competing forecasting approaches. Let’s imagine the situation, when we have four forecasting methods applied to 100 time series with accuracy measured in terms of RMSSE: […]
How confident are you? Assessing the uncertainty in forecasting
Introduction Some people think that the main idea of forecasting is in predicting the future as accurately as possible. I have bad news for them. The main idea of forecasting is in decreasing the uncertainty. Think about it: any event that we want to predict has some systematic components \(\mu_t\), which could potentially be captured […]
Are you sure you’re precise? Measuring accuracy of point forecasts
Two years ago I have written a post “Naughty APEs and the quest for the holy grail“, where I have discussed why percentage-based error measures (such as MPE, MAPE, sMAPE) are not good for the task of forecasting performance evaluation. However, it seems to me that I did not explain the topic to the full […]
useR!2019 in Toulouse, France
Salut mes amis! Today I’ve presented my smooth package at the useR!2019 conference in Toulouse, France. This is a nice conference, focused on specific solutions to specific problems. Here, people tend to present functions from their packages (not underlying models, like, for example, at ISF). On one hand, this has its own limitations, but on […]
Analytics with greybox
One of the reasons why I have started the greybox package is to use it for marketing research and marketing analytics. The common problem that I face, when working with these courses is analysing the data measured in different scales. While R handles numeric scales natively, the work with categorical is not satisfactory. Yes, I […]
“smooth” package for R. Intermittent state-space model. Part I. Introducing the model
UPDATE: Starting from smooth v 3.0.0, the occurrence part of the model has been removed from es() and other functions. The only one that implements this now is adam(). This post has been updated on 01 January 2021. UPDATE: Starting from smooth v 2.5.0, the model and the respective functions have changed. Now instead of […]
greybox 0.3.0 – what’s new
Three months have passed since the initial release of greybox on CRAN. I would not say that the package develops like crazy, but there have been some changes since May. Let’s have a look. We start by loading both greybox and smooth: library(greybox) library(smooth) Rolling Origin First of all, ro() function now has its own […]
greybox package for R
I am delighted to announce a new package on CRAN. It is called “greybox”. I know, what my American friends will say, as soon as they see the name – they will claim that there is a typo, and that it should be “a” instead of “e”. But in fact no mistake was made – […]
Comparing additive and multiplicative regressions using AIC in R
One of the basic things the students are taught in statistics classes is that the comparison of models using information criteria can only be done when the models have the same response variable. This means, for example, that when you have \(\log(y_t)\) and calculate AIC, then this value is not comparable with AIC from a […]