Error Measures Flow Chart

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In order to help master students of Lancaster University Managemen Science department, I have developed a flow chart, that acts as a basic guide on what error measures to use in different circumstances. This is not a complete and far from perfect flow chart, and it assumes that the decision maker knows what intermittent demand … Read more

ISF2021: How to Make Multiplicative ETS Work for You

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This year International Symposium on Forecasting was held online, although Centre for Marketing Analytics and Forecasting of Lancaster University had their own hub, where we would come and watch presentations together and even present to the others. I presented on the topic of Multiplicative ETS, based on this chapter of the ADAM textbook and on … Read more

Multi-step Estimators and Shrinkage Effect in Time Series Models – presentation for CEBA

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Today I have made a presentation on the topic of “Multi-step Estimators and Shrinkage Effect in Time Series Models” for Center for Econometrics and Business Analytics (CEBA) of St.Petersburg State University. This presentation was based on the paper with the similar name written by Ivan Svetunkov, Nikolaos Kourentzes and Rebecca Killick. In the presentation, I … Read more

After the creation of ADAM: smooth v3.1.0

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Since the previous post on “The Creation of ADAM“, I had difficulties finding time to code anything, but I still managed to fix some bugs, implement a couple of features and make changes, important enough to call the next version of package smooth “3.1.0”. Here is what’s new: A new algorithm for ARIMA order selection … Read more

The creation of ADAM – next step in statistical forecasting

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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 … Read more

Accuracy of forecasting methods: Can you tell the difference?

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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: … Read more

Forecasting method vs forecasting model: what’s difference?

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If you work in the field of statistics, analytics, data science or forecasting, then you probably have already noticed that some of the instruments that are used in your field are called “methods”, while the others are called “models”. The issue here is that the people, using these terms, usually know the distinction between them, … Read more

M-competitions, from M4 to M5: reservations and expectations

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UPDATE: I have also written a short post on “The role of M competitions in forecasting“, which gives historical perspective and a brief overview of the main findings of the previous competitions. Some of you might have noticed that the guidelines for the M5 competition have finally been released. Those of you who have previously … Read more

Multiplicative State-Space Models for Intermittent Time Series, 2019

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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