Functional time series forecasting with dynamic updating: An application to intraday particulate matter concentration

详细信息    查看全文

• 作者：Han Lin Shang ; ^{hanlin.shang@anu.edu.au}
• 关键词：Block moving ; Dynamic updating ; Functional principal component regression ; Functional linear regression ; Maximum entropy bootstrap ; VAR
• 刊名：Econometrics and Statistics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：1
• 期：Complete
• 页码：184-200
• 全文大小：1324 K
• 卷排序：1

文摘

Functional principal component regression for modeling and forecasting a sliced functional time series is considered. When new data points in the most recent curve are observed, dynamic updating methods are introduced to improve point and interval forecast accuracies. Via simulation and a data set, we found the vector autoregressive model outperforms the autoregressive integrated moving average model for forecasting principal component scores. Via simulation and a data set, a robust functional principal component analysis ought to be used when functional curves contain outliers.