Blog Archives

How confident are you? Assessing the uncertainty in forecasting

October 18, 2019
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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

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Are you sure you’re precise? Measuring accuracy of point forecasts

August 25, 2019
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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

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useR!2019 in Toulouse, France

July 11, 2019
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Salut mes amis! Today I’ve presented my 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

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Marketing analytics with greybox

January 7, 2019
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Marketing analytics with greybox

One of the reasons why I have started the 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

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«smooth» package for R. Intermittent state-space model. Part I. Introducing the model

September 18, 2018
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«smooth» package for R. Intermittent state-space model. Part I. Introducing the model

Intro One of the features of functions of smooth package is the ability to work with intermittent data and the data with periodically occurring zeroes. Intermittent time series is a series that has non-zero values occurring at irregular frequency (Svetuknov and Boylan, 2017). Imagine retailer who sells green leap sticks. The demand on such a

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greybox 0.3.0 – what’s new

August 7, 2018
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greybox 0.3.0 – what’s new

Three months have passed since the initial release of 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 and : Rolling Origin First of all, function now has its own class

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greybox package for R

May 4, 2018
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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 –

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Comparing additive and multiplicative regressions using AIC in R

March 22, 2018
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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

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«smooth» package for R. Common ground. Part IV. Exogenous variables. Advanced stuff

February 10, 2018
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«smooth» package for R. Common ground. Part IV. Exogenous variables. Advanced stuff

Previously we’ve covered the basics of exogenous variables in smooth functions. Today we will go slightly crazy and discuss automatic variables selection. But before we do that, we need to look at a Santa’s little helper function implemented in . It is called . It is useful in cases when you think that your exogenous

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«smooth» package for R. Common ground. Part III. Exogenous variables. Basic stuff

January 15, 2018
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«smooth» package for R. Common ground. Part III. Exogenous variables. Basic stuff

One of the features of the functions in smooth package is the ability to use exogenous (aka “external”) variables. This potentially leads to the increase in the forecasting accuracy (given that you have a good estimate of the future exogenous variable). For example, in retail this can be a binary variable for promotions and we

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