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Forecasting
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Application of Functional Data Analysis in Forecasting
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Application of Functional Data Analysis in Forecasting

A special issue of Forecasting (ISSN 2571-9394).

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 3787

Special Issue Editors

Dr. Yang Yang

E-Mail Website
Guest Editor
Department of Data Science and Innovation, School of Information and Physical Sciences, University of Newcastle, Callaghan, NSW 2308, Australia
Interests: functional data analysis; demography forecasting; time series models; panel data models; climate data analysis
Dr. Wenlin Dai

E-Mail Website
Guest Editor
Institute of Statistics and Big Data, Renmin University of China, Beijing 100872, China
Interests: functional data analysis; nonparametric regression; network data analysis; data reduction

Special Issue Information

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.

Keywords

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

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Published Papers (2 papers)

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Research

24 pages, 4621 KiB  
Article
An In-Depth Look at Rising Temperatures: Forecasting with Advanced Time Series Models in Major US Regions
by Kameron B. Kinast and Ernest Fokoué
Forecasting 2024, 6(3), 815-838; https://doi.org/10.3390/forecast6030041 - 18 Sep 2024
Viewed by 1063
Abstract
With growing concerns over climate change, accurately predicting temperature trends is crucial for informed decision-making and policy development. In this study, we perform a comprehensive comparative analysis of four advanced time series forecasting models—Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Multilayer Perceptron [...] Read more.
With growing concerns over climate change, accurately predicting temperature trends is crucial for informed decision-making and policy development. In this study, we perform a comprehensive comparative analysis of four advanced time series forecasting models—Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Multilayer Perceptron (MLP), and Gaussian Processes (GP)—to assess changes in minimum and maximum temperatures across four key regions in the United States. Our analysis includes hyperparameter optimization for each model to ensure peak performance. The results indicate that the MLP model outperforms the other models in terms of accuracy for temperature forecasting. Utilizing this best-performing model, we conduct temperature projections to evaluate the hypothesis that the rates of change in temperatures are greater than zero. Our findings confirm a positive rate of change in both maximum and minimum temperatures, suggesting a consistent upward trend over time. This research underscores the critical importance of refining time series forecasting models to address the challenges posed by climate change and supporting the development of effective strategies to mitigate the impacts of rising temperatures. The insights gained from this work emphasize the need for continuous advancement in predictive modeling techniques to better understand and respond to the dynamics of climate change. Full article
(This article belongs to the Special Issue Application of Functional Data Analysis in Forecasting)
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14 pages, 1093 KiB  
Article
Bootstrapping Long-Run Covariance of Stationary Functional Time Series
by Han Lin Shang
Forecasting 2024, 6(1), 138-151; https://doi.org/10.3390/forecast6010008 - 5 Feb 2024
Viewed by 1856
Abstract
A key summary statistic in a stationary functional time series is the long-run covariance function that measures serial dependence. It can be consistently estimated via a kernel sandwich estimator, which is the core of dynamic functional principal component regression for forecasting functional time [...] Read more.
A key summary statistic in a stationary functional time series is the long-run covariance function that measures serial dependence. It can be consistently estimated via a kernel sandwich estimator, which is the core of dynamic functional principal component regression for forecasting functional time series. To measure the uncertainty of the long-run covariance estimation, we consider sieve and functional autoregressive (FAR) bootstrap methods to generate pseudo-functional time series and study variability associated with the long-run covariance. The sieve bootstrap method is nonparametric (i.e., model-free), while the FAR bootstrap method is semi-parametric. The sieve bootstrap method relies on functional principal component analysis to decompose a functional time series into a set of estimated functional principal components and their associated scores. The scores can be bootstrapped via a vector autoregressive representation. The bootstrapped functional time series are obtained by multiplying the bootstrapped scores by the estimated functional principal components. The FAR bootstrap method relies on the FAR of order 1 to model the conditional mean of a functional time series, while residual functions can be bootstrapped via independent and identically distributed resampling. Through a series of Monte Carlo simulations, we evaluate and compare the finite-sample accuracy between the sieve and FAR bootstrap methods for quantifying the estimation uncertainty of the long-run covariance of a stationary functional time series. Full article
(This article belongs to the Special Issue Application of Functional Data Analysis in Forecasting)
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