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kloppy 3.14.0 is out

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Whoop. kloppy 3.14.0 is out!

This release includes lots of fixes and improvements to the Wyscout, Statsbomb and Opta code

Do We Really Need Deep Learning Models for Time Series Forecasting?

arxiv.org - Shereen Elsayed, Daniela Thyssens, Ahmed Rashed, Hadi Samer Jomaa, Lars Schmidt-Thieme

Abstract:Time series forecasting is a crucial task in machine learning, as it has a wide range of applications including but not limited to forecasting electricity consumption, traffic, and air quality. Traditional forecasting models rely on rolling averages, vector auto-regression and auto-regressive integrated moving averages. On the other hand, deep learning and matrix factorization models have been recently proposed to tackle the same problem with more competitive performance. However, one major drawback of such models is that they tend to be overly complex in comparison to traditional techniques. In this paper, we report the results of prominent deep learning models with respect to a well-known machine learning baseline, a Gradient Boosting Regression Tree (GBRT) model. Similar to the deep neural network (DNN) models, we transform the time series forecasting task into a window-based regression problem. Furthermore, we feature-engineered the input and output structure of the GBRT model, such that, for each training window, the target values are concatenated with external features, and then flattened to form one input instance for a multi-output GBRT model. We conducted a comparative study on nine datasets for eight state-of-the-art deep-learning models that were presented at top-level conferences in the last years. The results demonstrate that the window-based input transformation boosts the performance of a simple GBRT model to levels that outperform all state-of-the-art DNN models evaluated in this paper.

What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting

ametsoc.org - Allan H. Murphy

Differences of opinion exist among forecasters—and between forecasters and users—regarding the meaning of the phrase “good (bad) weather forecasts.” These differences of opinion are fueled by a lack of clarity and/or understanding concerning the nature of goodness in weather forecasting. This lack of clarity and understanding complicates the processes of formulating and evaluating weather forecasts and undermines their ultimate usefulness.Three distinct types of goodness are identified in this paper: 1) the correspondence between forecasters’ judgments and their forecasts (type 1 goodness, or consistency), 2) the correspondence between the forecasts and the matching observations (type 2 goodness, or quality), and 3) the incremental economic and/or other benefits realized by decision makers through the use of the forecasts (type 3 goodness, or value). Each type of goodness is defined and described in some detail. In addition, issues related to the measurement of consistency, quality, and value are discussed.Relationships among the three types of goodness are also considered. It is shown by example that the level of consistency directly impacts the levels of both quality and value. Moreover, recent studies of quality/value relationships have revealed that these relationships are inherently nonlinear and may not be monotonic unless the multifaceted nature of quality is respected. Some implications of these considerations for various practices related to operational forecasting are discussed. Changes in these practices that could enhance the goodness of weather forecasts in one or more respects are identified.SaveEmail this content

Sports Analytics Weekly by kubeia.io - 52/2023

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