Bayesian Time Series Forecasting
A special issue of Forecasting (ISSN 2571-9394).
Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 4131
Special Issue Editors
Interests: forecasting bayesian learning; optimization deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; biomedicine; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: neurocontrollers; data modeling; time series forecasting; dynamic process modelling
Special Issues, Collections and Topics in MDPI journals
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, Collections and Topics 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 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
- Bayesian inference
- Bayesian method
- time series model
- forecasting
- machine learning
- deep learning
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