Mastodawn

We continue our series of posts on the functions from the smooth package for Python/R. Today we will see how to enhance your exponential smoothing with explanatory variables. What? Yes, you heard me! Let’s dive in!

https://openforecast.org/2026/05/05/smooth-in-python-ets-with-explanatory-variables/

#python #datascience #machinelearning #forecasting

smooth in python: ETS with explanatory variables - Open Forecasting

We continue our series of posts on the functions from the smooth package for Python/R. Today we will see how to enhance your exponential smoothing with explanatory variables. What? Yes, you heard me! Let’s dive in! We all know that in real life sales don’t just evolve over time on their own. Any univariate model, […]

Open Forecasting

Ivan Svetunkov Apr 27

Last time we saw how to do automated model selection using the ES function from the smooth package. Now I want to show how to produce combined forecasts from ETS.

https://openforecast.org/2026/04/27/smooth-in-python-ets-forecast-combination/

#forecasting #datascience #machinelearning #ml #python

smooth in python: ETS forecast combination - Open Forecasting

Last time we saw how to do automated model selection using the ES function from the smooth package. Now I want to show how to produce combined forecasts from ETS. Why bother? There is a vast body of literature on forecast combinations (read this great review). The main idea is that you should not put […]

Open Forecasting

Ivan Svetunkov Apr 24

As some of you have heard, the smooth package is now on PyPI. So, I’ve decided to write a series of posts showcasing how some of its functions work. We start with the basics, ETS.

https://openforecast.org/2026/04/22/smooth-in-python-ets-with-model-selection/

#forecasting #datascience #machinelearning #python

smooth in python: ETS with model selection - Open Forecasting

As some of you have heard, the smooth package is now on PyPI. So, I’ve decided to write a series of posts showcasing how some of its functions work. We start with the basics, ETS. ETS stands for the “Error-Trend-Seasonal” model or ExponenTial Smoothing. It is a statistical model that relies on time series decomposition […]

Open Forecasting

Ivan Svetunkov Apr 13

Great news, everyone! After years of development in R, the first release of the smooth forecasting package is now available for Python. Why is this great news? I explain it in the following post:

https://openforecast.org/2026/04/09/smooth-forecasting-with-the-smooth-package-in-python/

#forecasting #python #machinelearning #datascience

smooth forecasting with the smooth package in Python - Open Forecasting

Here is another piece of news I have been hoping to deliver for quite some time now (since January 2026 actually). We have finally created the first release of the smooth package for Python and it is available on PyPI! Anyone interested? Read more! On this page: Why does “smooth” exist? A bit of history […]

Open Forecasting

Ivan Svetunkov Mar 2

There’s no such thing as “deterministic forecast”!

Sometimes I see people referring to a “deterministic” forecast, and I have some personal issues with this. Because if you apply a model to data then there is nothing deterministic about your forecasts!

https://openforecast.org/2026/03/02/there-s-no-such-thing-as-deterministic-forecast/

#forecasting #datascience #machinelearning

There's no such thing as "deterministic forecast" - Open Forecasting

Sometimes I see people referring to a “deterministic” forecast, and I have some personal issues with this. Because if you apply a model to data then there is nothing deterministic about your forecasts! In many contexts, “deterministic” has a precise meaning: no randomness, no uncertainty. A deterministic solution to an optimisation problem (e.g. linear programming) […]

Open Forecasting

Ivan Svetunkov Feb 23

Apparently, we need to talk about scaling of error measures because this is not as obvious as it seems: https://openforecast.org/2026/02/23/scaling-of-error-measures/

#forecasting #machinelearning #datascience

Scaling of error measures - Open Forecasting

Apparently, we need to talk about scaling of error measures because this is not as obvious as it seems. In forecasting literature, since early days of the area, there has been a general consensus that the forecast errors from the individual time series should not be analysed and aggregated as is. This is because you […]

Open Forecasting

Ivan Svetunkov Feb 9

Great news, everyone! smooth package for R version 4.4.0 is now on CRAN. Why is this a great news? Let me explain!

https://openforecast.org/2026/02/09/smooth-v4-4-0/

#forecasting #datascience #rstats #machinelearning