Is the Simple Moving Average a good forecasting method? What evidence says, how to choose the order, and when to move to better models.

Is forecastability a useful concept? Five assumptions behind every forecastability measure, why CoV misleads, and what to ask instead.

Why AIC comparison fails for ARIMA models with different differencing, and how the state space formulation in smooth makes it valid on one sample.

Classical SARIMA handles one seasonal cycle. MSARIMA in the smooth Python package handles several at once - here is how, on half-hourly electricity data.

Seasonal Naive is one of the simplest forecasting methods, and should be used as a benchmark whenever you have seasonality in your data.

Why train a forecasting model on one-step-ahead errors when you care about 10-step-ahead accuracy? This is the core motivation behind multistep losses in dynamic models. This has connection with the s...

The Naive forecasting method predicts the last observed value. Simple, yet surprisingly hard to beat — and the benchmark every forecast evaluation needs.

This time, at ISF2026, I presented the paper that I have worked on together with Juan Ramon Trapero and Diego Pedregal. The idea of the paper is to introduce a taxonomy of the models in the Multiple S...

Here is a curious idea: if we can somehow estimate the importance of trend/seasonal components for your data, you can use this in model building and forecasting. But how can we do this first step? Han...

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! ...

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 l...

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 “...

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 ...

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...

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...

Great news, everyone! smooth package for R version 4.4.0 is now on CRAN. Why is this a great news? Let me explain! On this page: What’s new? Evaluation Setup Results What’s next? Here is what’s new si...

Here is one small, unexpected piece of news: I now have my first package on PyPI! It’s called fcompdata, and let me tell you a little bit about it. When I test my functions in R, I usually use the M1,...

What do you use for model selection? Do you select the best model based on its cross-validated performance, or do you use in-sample measures like AIC? If so, there is a way to improve your selection p...

Teaching statistics as a flipped classroom with the help of AI? You heard that right! That’s exactly what I tried this year – and here are the results. Attached to this post is the student evaluation ...

When I read posts written by some ML experts, I sometimes notice that they either overlook or do not clearly explain a few crucial steps in demand forecasting. In this post, I want to highlight the th...

Valeriy Manokhin, PhD, MBA, CQF is a toxic dishonest individual posing as a data science expert. Due to his extensive activity on social media, he has way too many followers. If you're following him, ...

This year, I couldn’t attend the International Symposium on Forecasting (organised by the International Institute of Forecasters), which I usually do, so instead I went to Gran Canaria for the Interna...

Here is one thing you have probably not heard of: a workshop on Open Source Forecasting software, held in Beijing on 26th – 27th June 2025. This was a closed event, with speakers attending by invitati...

Are there any PhD students in the crowd who want to learn more about forecasting? What about academic supervisors who have such students? Show me your hands! This post is for you! This September, we (...

There’s a large and fascinating area in time series analysis called “changepoint detection”. I hadn’t worked in this area before, but thanks to Rebecca Killick and Thomas Grundy, I contributed to the ...

I’ve been acting as a reviewer lately, providing comments on papers about intermittent demand, and I’ve felt a bit frustrated by what some authors write. Let me explain. Several papers I reviewed clai...

NHS has a helpful page with a set of questions you can ask your GP to ensure you receive the right treatment for your illness. Surprisingly, these questions can be applied in other fields as well. Her...

If you have taken a course on forecasting or time series analysis, you’ve probably heard of ARIMA and the Box–Jenkins methodology. In my opinion, this methodology has a fundamental flaw and should not...

It was a pleasure to attend the 5th IMA and OR Society Conference at Aston University, Birmingham, and to present my research with Anna Sroginis on model-based demand classification. A great crowd of ...

A research-backed AI scenario forecast.

For the last year, Anna Sroginis and I have been working on a paper, trying to modernise demand classification schemes and make them useful in the brave new era of machine learning. We have finally wr...

There are many different issues with capturing seasonality in time series. In this short post, I’d like to discuss one of the most annoying ones. I’m talking about the seasonal pattern that shifts ove...

If someone tells you that method X solves all problems and is the best one ever, they are either lying intentionally or do not fully understand what they are talking about. There is no such thing as “...

If you follow certain influencers on LinkedIn, you might have come across the term “forecasting academia” (or “applied forecasting academia”). If you’re not familiar with the field, you might not know...

Image above depicts a fashion model making a presentation about a forecasting method. I like the forecast for the final period in that image… Over the last few years, I’ve seen phrases like “LightGBM ...

I’m currently doing a literature review for one of my papers on intermittent demand forecasting with machine learning, and I’ve noticed a recurring fundamental mistake in several recently published pa...

Christmas and the New Year are upon us, and I wanted to publish a celebratory post before taking a break. Instead of writing something educational, I decided to simply recommend a paper for you to rea...

I’ve seen several times ML experts applying principles of classification for intermittent demand forecasting. For example, they try predicting, WHEN the demand will happen. This is not a very sensible...

While Naive is considered a standard benchmark in forecasting, there is a case where it might not be a good one: intermittent demand. And here is why I think so. Naive is a forecasting method that use...

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