Multiplicative State-Space Models for Intermittent Time Series, 2019

More than 2 years ago I published on this website a working paper entitled “Multiplicative State-Space Models for Intermittent Time Series“, written by John Boylan and I. This was an early version of the paper, which we submitted to International Journal of Forecasting on 31st January 2017. More than two years later (on 11th July … Read more

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 … Read more

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 … Read more

“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 … Read more

“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 smooth. It is called xregExpander(). It is useful in cases when you think that your exogenous … Read more

“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 … Read more

“smooth” package for R. Common ground. Part II. Estimators

UPDATE: Starting from the v2.5.1 the cfType parameter has been renamed into loss. This post has been updated since then in order to include the more recent name. A bit about estimates of parameters Hi everyone! Today I want to tell you about parameters estimation of smooth functions. But before going into details, there are … Read more

Old dog, new tricks: a modelling view of simple moving averages

Fotios Petropoulos and I have recently written a paper about a statistical model, underlying Simple Moving Average. Although we are usually taught in Forecasting courses, that there is no such thing, we found one. We have submitted this paper to International Journal of Production Research, and it has been recently accepted (took us ~4 months). … Read more