Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
3.0
Journals
Forecasting
Special Issues
New Advances in Time Series and Forecasting
share announcement

New Advances in Time Series and Forecasting

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Forecasting in Computer Science".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 3787

Special Issue Editors

Prof. Dr. Ignacio Rojas

E-Mail Website
Guest Editor
Information Technology and Telecommunications Engineering, University of Granada, 18010 Granada, Spain
Interests: time series analysis; forecasting; artificial intelligence; biomedical engineering; complex problem
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis Javier Herrera

E-Mail Website
Guest Editor
Department of Computer Architecture and Computer Technology, Higher Technical School of Information Technology and Telecommunications Engineering, CITIC-UGR, University of Granada, 18011 Granada, Spain
Interests: time series; forecasting; complex systems; bioinformatics
Prof. Dr. Hector Pomares

E-Mail Website
Guest Editor
Department of Computer Architecture and Computer Technology, Higher Technical School of Information Technology and Telecommunications Engineering, CITIC-UGR, University of Granada, 18011 Granada, Spain
Interests: time series; forecasting; complex systems; HPC
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Olga Valenzuela

E-Mail Website
Guest Editor
Department of Applied Mathematics, University of Granada, 18071 Granada, Spain
Interests: deep learning; statistical analysis in big data; machine learning algorithms; data mining; bioinformatics; computational biology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For this Special Issue of Forecasting, we are soliciting high-quality original research papers on any aspect of time series analysis and forecasting, with the aim of motivating the generation and use of knowledge and new computational techniques and methods on forecasting in a wide range of fields. The topics of interest include but are not limited to:

  • Time series analysis and forecasting
  • Econometrics and forecasting
  • Advanced methods and online learning in time series
  • High dimensional and complex/big data
  • Forecasting in real problems

Prof. Dr. Ignacio Rojas
Prof. Dr. Luis Javier Herrera
Prof. Dr. Hector Pomares
Prof. Dr. Olga Valenzuela
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

  • time series analysis and forecasting
  • econometrics and forecasting
  • advanced methods and online learning in time series
  • high dimensional and complex/big data
  • forecasting in real problems

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 8172 KiB  
Article
Precision and Reliability of Forecasts Performance Metrics
by Philippe St-Aubin and Bruno Agard
Forecasting 2022, 4(4), 882-903; https://doi.org/10.3390/forecast4040048 - 30 Oct 2022
Cited by 3 | Viewed by 2335
Abstract
The selection of an accurate performance metric is highly important to evaluate the quality of a forecasting method. This evaluation may help to select between different forecasting tools of forecasting outputs, and then support many decisions within a company. This paper proposes to [...] Read more.
The selection of an accurate performance metric is highly important to evaluate the quality of a forecasting method. This evaluation may help to select between different forecasting tools of forecasting outputs, and then support many decisions within a company. This paper proposes to evaluate the sensitivity and reliability of forecasts performance metrics. The methodology is tested using multiple time series of different scales and demand patterns, such as intermittent demand. The idea is to add to each series a noise following a known distribution to represent forecasting models of a known error distribution. Varying the parameters of the distribution of the noise allows to evaluate how sensitive and reliable performance metrics are to changes in bias and variance of the error of a forecasting model. The experiments concluded that sRMSE is more reliable than MASE in most cases on those series. sRMSE is especially reliable for detecting changes in the variance of a model and sPIS is the most sensitive metric to the bias of a model. sAPIS is sensible to both variance and bias but is less reliable. Full article
(This article belongs to the Special Issue New Advances in Time Series and Forecasting)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop