Multistep loss functions: Geometric Trace MSE

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 […]

Multistep loss functions: Trace MSE

As we discussed last time, there are two possible strategies in forecasting: recursive and direct. The latter aligns with the estimation of a model using a so-called multistep loss function, such as Mean Squared Error for h-steps-ahead forecast (MSEh). But this is not the only loss function that can be efficiently used for model estimation. […]

Recursive vs Direct Forecasting Strategy

Have you heard about the recursive vs direct forecasts? There’s literature about them in the areas of both ML and statistics. What’s so special about them? Here is a short post. The term “recursive” forecasting refers to the approach, when we produce one-step-ahead forecast first, then use it to produce two-steps-ahead, three-steps-ahead, and so on. […]

What’s wrong with ARIMA?

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 […]

Why you should not use Holt-Winters method

Whenever I see results of an experiment that include Holt-Winters method, I shrug. You should not use it, and here is why. Holt-Winters was developed in 1960 by a student of Charles Holt, Peter Winters (Winters, 1960). He extended Holt’s exponential smoothing method (the method that introduced a trend component) to include a seasonal component. […]

Multi-step Estimators and Shrinkage Effect in Time Series Models

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 […]

Vector Exponential Smoothing with PIC restrictions

About the paper. Introduction When you follow academics on social media, you typically see many success stories. This person published a paper in Management Science; another one published in EJOR; your colleague from a different university created a great package; and there is also an academic who is ten years younger than you and has […]

An Integrated Method for Estimation and Optimisation

My PhD student, Congzheng Liu (co-supervised with Adam Letchford) has written a paper, entitled “Newsvendor Problems: An Integrated Method for Estimation and Optimisation“. This paper has recently been published in EJOR. In this paper we build upon the existing Ban & Rudin (2019) approach for newsvendor problem, showing that in case of the linear model, […]

Stochastic coherency in forecast reconciliation

My student (co-supervised with Nikos Kourentzes), Kandrika F. Pritularga has written a paper on “Stochastic coherency in forecast reconciliation”, which has been recently published in International Journal of Production Economics (here it is). This paper contributes to the field of hierarchical forecasting, the main issue of which is that the forecasts produced on different levels […]