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Forecasting: A New Open Access Journal Dealing with Time Series Analysis and Forecasting

Department of Computer Architecture and Computer Technology, CITIC-UGR, University of Granada, 18071 Granada, Spain
Forecasting 2019, 1(1), 1-2;
Received: 5 March 2018 / Revised: 6 March 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
Welcome to Forecasting, a new, online, open access journal, which provides an advanced forum for studies related to forecasting: theoretical, practical, computational and methodological. It publishes regular research papers, communications, letters, short notes and reviews.
The main editorial objective for Forecasting is to provide a forum for advanced research, with topics of interest including, but not limited to, methodology of forecasting, forecasting complex/big data, time series analysis and forecasting, evaluation and implementation of forecasting methods and approaches, hybrid advanced methodologies with evolutionary computation techniques to improve forecasting accuracy, limitations of forecasting, etc. Also relevant is the application of forecasting in different areas, including (but not limited to) atmospheric science forecasting, climate forecasting, economic and econometric forecasting, demand planning, supply chain management, energy forecasting, hydrological forecasting, health forecasting, industrial production values forecasting, marketing forecasting, software reliability forecasting, traffic forecasting, and tourism forecasting.
From the measurements of natural phenomena such as the number of rays in a storm, the amount of rainfall or temperature in a given region, to the electricity consumption of a city or a country, they can all be considered as a mere succession of values obtained, normally at regular intervals, or a time series. The usefulness of the study of time series and forecasting is of huge importance for our society in general. At this point, it should be noted that there are many types of time series caused by different phenomena, and there therefore exist many different behaviors. If the phenomena behind a series were known with enough accuracy, we would be able to build a model from them, making prediction easy, direct and accurate. However, most phenomena are not known or, at least, are not known with sufficient precision; thus, obtaining a model from the phenomena becomes unfeasible, unless we are able to infer the model from the values of the series in order to forecast future values.
The applications in this field are enormous, and include energy forecasting or economic analysis of stock indices prediction, climate forecasting, chemical or natural process forecasting, etc. It can be affirmed that forecasting is a transversal field of research encompassing multiple disciplines.
Our aim is to encourage researchers to publish their experimental, theoretical and computational results in as much detail as necessary. Articles and review papers are also welcomed. There is no restriction on paper length or number of figures and tables, allowing authors to explain their scientific content thoroughly.
We would like to take this opportunity to thank all the authors, reviewers and Editorial Board members of Forecasting for their future contributions and help. Together, we can create and encourage the new journal Forecasting to become a premier interdisciplinary journal on forecasting and time series analysis. Our task is to realize a fast publication process for high-quality contributions on relevant topics within forecasting. We would also like to thank the MDPI Editorial Office for their support.
In conclusion, on behalf of the Editorial Board, let me cordially invite the international scientific community in the field of forecasting and time series analysis to choose this new exciting journal; we look forward to receiving your research contributions. Thank you all in advance.

Conflicts of Interest

The author declares no conflict of interest.

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MDPI and ACS Style

Rojas, I. Forecasting: A New Open Access Journal Dealing with Time Series Analysis and Forecasting. Forecasting 2019, 1, 1-2.

AMA Style

Rojas I. Forecasting: A New Open Access Journal Dealing with Time Series Analysis and Forecasting. Forecasting. 2019; 1(1):1-2.

Chicago/Turabian Style

Rojas, Ignacio. 2019. "Forecasting: A New Open Access Journal Dealing with Time Series Analysis and Forecasting" Forecasting 1, no. 1: 1-2.

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