New Advances in Forecasting Rainfall with Time Series Model

Dear Colleagues,

We invite you to submit your last research on the topic of New Advances in Forecasting Rainfall with Time Series Model.

This Special Issue elicits new research on the methods used to forecast rainfall with time series models in order to facilitate the selection of the appropriate forecast method according to needs. The methods could include statistical, computational intelligence like deep neural nets, etc., and numerical prediction, or some combination/hybridization of these methods. Typical rainfall forecasting is translated into single deterministic or an ensemble of short, intermediate, and long lead-time forecasts. Finally, in addition point forecasts, we need probabilistic forecasts of these models. Nevertheless, risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty associated forecasts, and it is often necessary to provide distributional rather than point forecasts.  Nowadays, with the availability of large numbers of data, it is possible to combine all the information and sources of uncertainty into a predictive distribution for future values.

This Special Issue will provide readers with up-to-date information on state-of-the-art methodology with perspectives of linking rainfall to meteorological forecasts. It will also foster discussion of innovative techniques, and studies on the following are invited in particular:

• Time Series Analysis
• Time Series Forecasting
• Evaluation of Forecasting Methods and Approaches
• Impact of Uncertainty on Decision Making
• Multivariate Time Series Modeling and Forecasting

Dr. Cristian Rodriguez Rivero
Dr. Leonardo Franco
Dr. Julian Antonio Pucheta
Dr. Hector Daniel Patino
Dr. Alvaro David Orjuela-Cañón
Prof. Dr. Gustavo E. Juárez
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.

• time series model
• rainfall
• forecasting
• computational intelligence