A review of flow field forecasting: A high‐dimensional forecasting procedure. (21st February 2020)

• Record Type: Journal Article
• Authors: Caudle, Kyle; Fleming, Patrick S.; Hoover, Randy C.
• Is Part Of: Wiley interdisciplinary reviews. Volume 13:Number 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-21
• Subjects: Forecasting -- time series -- statistical models; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1505
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract: Forecasting, especially high‐dimensional forecasting, is becoming more and more sought after, particularly as computing resources increase in both size and speed. Flow field forecasting is a general purpose regression‐based forecasting method that has recently been expanded to high‐dimensional settings. In this article, we provide an overview of the flow field forecasting methodology, with a particular emphasis on environments where the number of candidate predictor variables is large, potentially larger than the number of observations. This article is categorized under: Statistical Models > Time Series Models Statistical Learning and Exploratory Methods of the Data Sciences > Classification and Regression Trees Statistical and Graphical Methods of Data Analysis > Data Reduction, Smoothing, and Filtering Abstract : Forecasting, especially high‐dimensional forecasting, is becoming more and more sought after, particularly as computing resources increase in both size and speed. Flow field forecasting is a general purpose regression‐based forecasting method that has recently been expanded to high‐dimensional settings. In this article, we provide an overview of the flow field forecasting methodology, with a particular emphasis on environments where the number of candidate predictor variables is large, potentially larger than the number of observations.