ARIMA

smooth v4.3.0 in R: what’s new and what’s next?

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Sticker of the smooth package for R

Good news! The smooth package v4.3.0 is now on CRAN. And there are several things worth mentioning, so I have written this post. New default initialisation mechanism Since the beginning of the package, the smooth functions supported three ways for initialising the state vector (the vector that includes level, trend, seasonal indices): optimisation, backcasting and … Read more

Fundamental Flaw of the Box-Jenkins Methodology

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Sarah by Yegor Kamelev

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 be used in practice. Here’s why. When Box and Jenkins wrote their book back in the 1960s, it was a very different … Read more

Multistep loss functions: Geometric Trace MSE

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While there is a lot to say about multistep losses, I’ve decided to write the final post on one of them and leave the topic alone for a while. Here it goes. Last time, we discussed MSEh and TMSE, and I mentioned that both of them impose shrinkage and have some advantages and disadvantages. One … Read more

Detecting patterns in white noise

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Back in 2015, when I was working on my paper on Complex Exponential Smoothing, I conducted a simple simulation experiment to check how ARIMA and ETS select components/orders in time series. And I found something interesting… One of the important steps in forecasting with statistical models is identifying the existing structure. In the case of … Read more

What does “lower error measure” really mean?

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“My amazing forecasting method has a lower MASE than any other method!” You’ve probably seen claims like this on social media or in papers. But have you ever thought about what it really means? Many forecasting experiments come to applying several approaches to a dataset, calculating error measures for each method per time series and … Read more

What’s wrong with ARIMA?

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Have you heard of ARIMA? It is one of the benchmark forecasting models used in different academic experiments, although it is not always popular among practitioners. But why? What’s wrong with ARIMA? ARIMA has been a standard forecasting model in statistics for ages. It gained popularity with the famous Box & Jenkins (1970) book and … Read more

The role of M competitions in forecasting

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If you are interested in forecasting, you might have heard of M-competitions. They played a pivotal role in developing forecasting principles, yet also sparked controversy. In this short post, I’ll briefly explain their historical significance and discuss their main findings. Before M-competitions, only few papers properly evaluated forecasting approaches. Statisticians assumed that if a model … Read more

Multi-step Estimators and Shrinkage Effect in Time Series Models

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Authors: Ivan Svetunkov, Nikos Kourentzes, Rebecca Killick Journal: Computational Statistics Abstract: Many modern statistical models are used for both insight and prediction when applied to data. When models are used for prediction one should optimise parameters through a prediction error loss function. Estimation methods based on multiple steps ahead forecast errors have been shown to … Read more

smooth v3.2.0: what’s new?

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smooth package has reached version 3.2.0 and is now on CRAN. While the version change from 3.1.7 to 3.2.0 looks small, this has introduced several substantial changes and represents a first step in moving to the new C++ code in the core of the functions. In this short post, I will outline the main new … Read more