Application of Functional Data Analysis in Forecasting

Dear Colleagues,

The rapid advancement of automated data collection technology gives rise to functional data showcasing intricate trajectories in various areas. In the last decade, the modeling and forecasting of functional time series have attracted growing interest. Today, in many applications involving a large number of time series, precisely extracting features of data is essential for a full exploitation of the high-dimensional functional objects and for ultimately producing accurate forecasts.

Forecasting large datasets with complex and cross-correlated functional time series has been a relatively unexplored research topic despite the rapidly developing functional data analysis (FDA). For this reason, the aim of this Special Issue is to collect contributions about novel feature extraction methods and forecasting applications involving a large collection of functional time series. Papers focusing on theoretical properties or empirical applications of new functional time series forecasting methodologies are welcome for publication in this Special Issue.

For this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Forecasting high-dimensional functional time series;
• Forecasting multivariate functional time series;
• Forecasting high-frequency financial time series;
• Forecasting climate functional time series;
• Forecasting demographic functional time series, etc.

Dr. Yang Yang
Dr. Wenlin Dai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• functional data analysis
• functional time series analysis
• functional principal component analysis
• high-dimensional functional time series
• functional regression
• feature extraction