## Elucidate structure in intermittent demand time series

Nikolaos Kourentzes and George Athanasopoulos, 2020. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2020.05.046

Forecasting research

Nikolaos Kourentzes and George Athanasopoulos, 2020. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2020.05.046

The tsutils package for R includes functions that help with time series exploration and forecasting, that were previously included in the TStools package that is only available on github. The name change was necessary as there is another package on CRAN with the same name. The objective of TStools is to provide a development and… Read More »

Past experiments have demonstrated that humans (with or without formal training) are quite good at visually identifying the structure of time series. Trend is a key component, and arguably the most relevant to practice, as many of the forecasts that affect our lives have to do with potential increases or decreases of economic variables. Forecasters… Read More »

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 »

I made this little interactive demo for basic time series exploration and decomposition for my students. I uploaded it here in case someone else finds it useful. Some things to try: How does the seasonal plot looks like for seasonal and non-seasonal time series? How does the seasonal plot looks when the trend is not… Read More »

Seasonality is a common characteristic of time series. It can appear in two forms: additive and multiplicative. In the former case the amplitude of the seasonal variation is independent of the level, whereas in the latter it is connected. The following figure highlights this: Note that in the example of multiplicative seasonality the season is… Read More »

Amongst various minor improvements a few interesting new functions have been added to TStools package for R: Theta method ABC-XYZ analysis Theta method was found to be the most accurate in the M3 forecasting competition, but since then there has been limited use of the method. As it was later shown, the M3 competition Theta… Read More »

This is a collection of functions for time series analysis/modelling for R. Follow link to GitHub. If you need help installing this package in R have a look at this post. Alternatively just type in R the following commands: > if (!require(“devtools”)) install.packages(“devtools”) > devtools::install_github(“trnnick/TStools”) At the time of posting the following functions are included:… Read More »

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