Advances in Time Series Analysis and Forecasting

Dear Colleagues,

We are welcoming submissions for a Special Issue entitled “Advances in Time Series Analysis and Forecasting”. Collecting data over time occurs naturally in almost all fields of studies and it materializes in many ways. It can be collected continuously or discretely, equally or unequally spaced, with short or long time series, as univariate or high dimensional, with missing values, or with structural breaks, etc. Therefore, it is natural that the field encompasses many models and methods that are continuously evolving. The aim of this Special Issue is to present some of the most recent advances in the field. Theoretical papers and novel applications are welcome, with studies addressing the analysis of complex data, computational issues, forecasting, high dimensional studies, change of regime, outliers and robust methods, but submissions are not restricted to these topics. Review papers are also welcome.

Prof. Dr. Luiz Koodi Hotta
Prof. Dr. Pedro Luiz Valls Pereira
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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.

• econometrical and technical applications
• forecasting and assessment of forecasts
• multiple time series and cointegration
• time series with changes in regime
• stationarity of time series and long-term memory
• state–space models and Kalman filters
• filtering of stochastic processes
• forecasting
• risk analysis
• high dimensional
• outliers and robust methods
• complex data
• computational methods

Title: Time Series Forecasting with Many Predictors
Authors: Shuo-Chieh Huang; Ruey S Tsay
Affiliation: Booth School of Business, University of Chicago
Abstract: We propose a novel approach for time series forecasting with many predictors, referred to as the GO-sdPCA, in this paper. The approach employs a variable selection method known as the group orthogonal greedy algorithm and the high-dimensional Akaike information criterion to mitigate the impact of irrelevant predictors. Moreover, a novel technique, called peeling, is used to boost the variable selection procedure so that many factor-relevant predictors can be included in prediction. Finally, the supervised dynamic principal component analysis (sdPCA) method is adopted to account for the dynamic information in factor recovery. In simulation studies, we found that the proposed method adapts well to unknown degrees of sparsity and factor strength, which results in good performance even when the number of relevant predictors is large compared to the sample size. Applying to economic and environmental studies, the proposed method consistently performs well compared to some commonly used benchmarks in one-step-ahead out-sample forecasts.