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Time series analysis and forecasting with ECOTOOL

This paper presents ECOTOOL, a new free MATLAB toolbox that embodies several routines for identification, validation and forecasting of dynamic models. The toolbox includes a wide range of exploratory, descriptive and diagnostic statistical tools with visual support, designed in easy-to-use Graphica...

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Detalles Bibliográficos
Autor principal: Pedregal, Diego J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822939/
https://www.ncbi.nlm.nih.gov/pubmed/31671102
http://dx.doi.org/10.1371/journal.pone.0221238
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author Pedregal, Diego J.
author_facet Pedregal, Diego J.
author_sort Pedregal, Diego J.
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description This paper presents ECOTOOL, a new free MATLAB toolbox that embodies several routines for identification, validation and forecasting of dynamic models. The toolbox includes a wide range of exploratory, descriptive and diagnostic statistical tools with visual support, designed in easy-to-use Graphical User Interfaces. It also incorporates complex automatic procedures for identification, exact maximum likelihood estimation and outlier detection for many types of models available in the literature (like multi-seasonal ARIMA models, transfer functions, Exponential Smoothing, Unobserved Components, VARX). ECOTOOL is the outcome of a long period of programming effort with the aim of producing a user friendly toolkit such that, just a few lines of code written in MATLAB are able to perform a comprehensive analysis of time series. The toolbox is supplied with an in-depth documentation system and online help and is available on the internet. The paper describes the main functionalities of the toolbox, and its power is shown working on several real examples.
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spelling pubmed-68229392019-11-12 Time series analysis and forecasting with ECOTOOL Pedregal, Diego J. PLoS One Research Article This paper presents ECOTOOL, a new free MATLAB toolbox that embodies several routines for identification, validation and forecasting of dynamic models. The toolbox includes a wide range of exploratory, descriptive and diagnostic statistical tools with visual support, designed in easy-to-use Graphical User Interfaces. It also incorporates complex automatic procedures for identification, exact maximum likelihood estimation and outlier detection for many types of models available in the literature (like multi-seasonal ARIMA models, transfer functions, Exponential Smoothing, Unobserved Components, VARX). ECOTOOL is the outcome of a long period of programming effort with the aim of producing a user friendly toolkit such that, just a few lines of code written in MATLAB are able to perform a comprehensive analysis of time series. The toolbox is supplied with an in-depth documentation system and online help and is available on the internet. The paper describes the main functionalities of the toolbox, and its power is shown working on several real examples. Public Library of Science 2019-10-31 /pmc/articles/PMC6822939/ /pubmed/31671102 http://dx.doi.org/10.1371/journal.pone.0221238 Text en © 2019 Diego J. Pedregal http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Pedregal, Diego J.
Time series analysis and forecasting with ECOTOOL
title Time series analysis and forecasting with ECOTOOL
title_full Time series analysis and forecasting with ECOTOOL
title_fullStr Time series analysis and forecasting with ECOTOOL
title_full_unstemmed Time series analysis and forecasting with ECOTOOL
title_short Time series analysis and forecasting with ECOTOOL
title_sort time series analysis and forecasting with ecotool
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822939/
https://www.ncbi.nlm.nih.gov/pubmed/31671102
http://dx.doi.org/10.1371/journal.pone.0221238
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