Special Issue "Bayesian Time Series Forecasting"

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

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Dr. Cristian Rodriguez Rivero
E-Mail Website
Guest Editor
Faculty of Science, University of Amsterdam, 1012 WX Amsterdam, The Netherlands
Interests: forecasting bayesian learning; optimization deep learning
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Dr. Leonardo Franco
E-Mail Website
Guest Editor
Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Campus de Teatinos s/n, 29071 Málaga, Spain
Interests: artificial intelligence; deep learning; neural networks; data mining; smart sensors; time series forecasting
Special Issues and Collections in MDPI journals
Dr. Julian Antonio Pucheta
E-Mail Website
Guest Editor
LIMAC Laboratory, Universidad Nacional de Córdoba, Velez Sarsfield Ave., Cordoba 1611, Argentina
Interests: neurocontrollers; data modeling; time series forecasting; dynamic process modelling
Special Issues and Collections in MDPI journals
Dr. Hector Daniel Patino
E-Mail Website
Guest Editor
Instituto de Automática, Universidad Nacional de San Juan, Av. San Martín Oeste 1109, San Juan 5400, Argentina
Interests: computational intelligence applied to automation and robotics; optimal control based on adaptive dynamic programming; modeling and prediction of time series; weather modification
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research to this Special Issue on the topic of Bayesian Time Series Forecasting.

Since the early 1990s, the importance of Bayesian methods to the study of time series has increased rapidly. This has, no doubt, been ignited by an increase in appreciation for the advantages that Bayesian inference provides. It provides us with a formal way to incorporate the prior information before seeing the data in a natural manner, it fits perfectly with sequential learning and decision-making, and it directly leads to exact results from small samples. In addition, the Bayesian paradigm is particularly suited to prediction, since it takes into account all parameters and even model uncertainty. Nowadays, with the availability of large amounts of data, Bayesian analysis remains suitable for solving forecasting problems by combining all of the information and sources of uncertainty into a predictive distribution for future values.

Dr. Cristian Rodriguez Rivero
Dr. Leonardo Franco
Dr. Julian Antonio Pucheta
Dr. Hector Daniel Patino
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 papers will be 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 1000 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

  • Bayesian inference
  • Bayesian method
  • time series model
  • forecasting
  • machine learning
  • deep learning

Published Papers (1 paper)

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Research

Article
Bayesian Forecasting of Dynamic Extreme Quantiles
Forecasting 2021, 3(4), 729-740; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3040045 - 11 Oct 2021
Viewed by 228
Abstract
In this paper, we provide a novel Bayesian solution to forecasting extreme quantile thresholds that are dynamic in nature. This is an important problem in many fields of study including climatology, structural engineering, and finance. We utilize results from extreme value theory to [...] Read more.
In this paper, we provide a novel Bayesian solution to forecasting extreme quantile thresholds that are dynamic in nature. This is an important problem in many fields of study including climatology, structural engineering, and finance. We utilize results from extreme value theory to provide the backdrop for developing a state-space model for the unknown parameters of the observed time-series. To solve for the requisite probability densities, we derive a Rao-Blackwellized particle filter and, most importantly, a computationally efficient, recursive solution. Using the filter, the predictive distribution of future observations, conditioned on the past data, is forecast at each time-step and used to compute extreme quantile levels. We illustrate the improvement in forecasting ability, versus traditional methods, using simulations and also apply our technique to financial market data. Full article
(This article belongs to the Special Issue Bayesian Time Series Forecasting)
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