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Activity Number: 199 - SPEED: Data Expo
Type: Contributed
Date/Time: Monday, July 30, 2018 : 11:35 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #332597
Title: Applying functional data analysis and clustering methods on weather forecast data in the U.S.
Author(s): Yifan Wu and Ying (Daisy) Yu* and Chuyuan (Cherlane) Lin
Companies: Simon Fraser University and Simon Fraser University
Keywords: Weather Prediction; Functional Data Analysis; Functional Principal Component Analysis; Functional Linear Regression; Time-series Clustering

The objective of this study is to investigate the potential covariates correlated to the weather prediction performance in the U.S, especially to explore the spatial and time effects in the prediction accuracy. We performed the functional principal component analysis (FPCA) and time series clustering techniques to divide 50 U.S. states into clusters. Cluster-specific characteristics of weather prediction performance were visually detected and cluster-to-cluster differences were quantified in order to identify this most and least predictable U.S. states. Then we conducted a functional analysis to capture the main pattern of variance in the prediction error over time and further investigate how other weather-related variables correlate with the prediction accuracy.

Authors who are presenting talks have a * after their name.

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