Scalable Visualization Methods for Modern Generalized Additive Models. Issue 1 (2nd January 2020)

• Record Type: Journal Article
• Authors: Fasiolo, Matteo; Nedellec, Raphaël; Goude, Yannig; Wood, Simon N.
• Is Part Of: Journal of computational and graphical statistics. Volume 29:Issue 1(2020)
• Page Start: 78
• Page End: 86
• Publication Date: 2020-01-02
• Subjects: Electricity load forecasting -- Generalized additive models -- Interactive model building -- Regression modeling -- Residuals checking -- Visualization; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2019.1629942
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract: In the last two decades, the growth of computational resources has made it possible to handle generalized additive models (GAMs) that formerly were too costly for serious applications. However, the growth in model complexity has not been matched by improved visualizations for model development and results presentation. Motivated by an industrial application in electricity load forecasting, we identify the areas where the lack of modern visualization tools for GAMs is particularly severe, and we address the shortcomings of existing methods by proposing a set of visual tools that (a) are fast enough for interactive use, (b) exploit the additive structure of GAMs, (c) scale to large data sets, and (d) can be used in conjunction with a wide range of response distributions. The new visual methods proposed here are implemented by the mgcViz R package, available on the Comprehensive R Archive Network. Supplementary materials for this article are available online.