Developing academic software for teaching time series analysis : a case study

ABSTRACT: The academic training on time series analysis requires not only a sound theoretical background on the methods but also the use of specific academic software to appreciate the methods’ capabilities, limitations and proper applicability. It is desirable for students to program the routines a...

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Autores:
Cañón Barriga, Julio Eduardo
Valdés, Juan
González, Javier
Tipo de recurso:
Article of investigation
Fecha de publicación:
2009
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/34473
Acceso en línea:
https://hdl.handle.net/10495/34473
Palabra clave:
Desarrollo de programas para computador
Computer software - Development
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/2.5/co/
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dc.title.spa.fl_str_mv Developing academic software for teaching time series analysis : a case study
title Developing academic software for teaching time series analysis : a case study
spellingShingle Developing academic software for teaching time series analysis : a case study
Desarrollo de programas para computador
Computer software - Development
title_short Developing academic software for teaching time series analysis : a case study
title_full Developing academic software for teaching time series analysis : a case study
title_fullStr Developing academic software for teaching time series analysis : a case study
title_full_unstemmed Developing academic software for teaching time series analysis : a case study
title_sort Developing academic software for teaching time series analysis : a case study
dc.creator.fl_str_mv Cañón Barriga, Julio Eduardo
Valdés, Juan
González, Javier
dc.contributor.author.none.fl_str_mv Cañón Barriga, Julio Eduardo
Valdés, Juan
González, Javier
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Investigación en Gestión y Modelación Ambiental (GAIA)
dc.subject.lemb.none.fl_str_mv Desarrollo de programas para computador
Computer software - Development
topic Desarrollo de programas para computador
Computer software - Development
description ABSTRACT: The academic training on time series analysis requires not only a sound theoretical background on the methods but also the use of specific academic software to appreciate the methods’ capabilities, limitations and proper applicability. It is desirable for students to program the routines and algorithms by themselves but this is not always feasible, particularly during short courses and workshops in which the interest is to understand the information supplied by several analytical methods. Considering the time constraints and the need to stress the interpretative rather than the computational skills, the authors have developed the software package UATSA (University of Arizona Time Series Analysis) that incorporates many analytical tools commonly used in time series analysis in an organized and sequential manner: exploratory statistics, markovian processes, univariate and multivariate analyses (ARMA models), frequency decomposition algorithms, principal components, canonical correlations and cluster analyses are included within the current version of the package. UATSA is a stand-alone executable file compiled in MATLAB® that has been used in courses of time series analysis in hydrology at the University of Arizona and in several workshops offered by the authors between 2004 and 2007. The software aims to easily illustrate the use of algorithms in the synthesis and decomposition of time series, providing a background to the methods and a visual platform that is user friendly and data extensive. The software has evolved through the years, incorporating suggestions made by students to improve its appearance and widen its scope. The software also has contributed to a shift in teaching dynamics by allowing students and instructors to focus on interpreting and analyzing outcomes rather than just learning the set of mathematical tools.
publishDate 2009
dc.date.issued.none.fl_str_mv 2009
dc.date.accessioned.none.fl_str_mv 2023-04-05T18:25:01Z
dc.date.available.none.fl_str_mv 2023-04-05T18:25:01Z
dc.type.spa.fl_str_mv Artículo de investigación
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dc.identifier.issn.none.fl_str_mv 0736-8607
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url https://hdl.handle.net/10495/34473
dc.language.iso.spa.fl_str_mv eng
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dc.relation.citationendpage.spa.fl_str_mv 59
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dc.relation.citationstartpage.spa.fl_str_mv 49
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dc.relation.ispartofjournal.spa.fl_str_mv Computers in Education Division
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spelling Cañón Barriga, Julio EduardoValdés, JuanGonzález, JavierGrupo de Investigación en Gestión y Modelación Ambiental (GAIA)2023-04-05T18:25:01Z2023-04-05T18:25:01Z20090736-8607https://hdl.handle.net/10495/34473ABSTRACT: The academic training on time series analysis requires not only a sound theoretical background on the methods but also the use of specific academic software to appreciate the methods’ capabilities, limitations and proper applicability. It is desirable for students to program the routines and algorithms by themselves but this is not always feasible, particularly during short courses and workshops in which the interest is to understand the information supplied by several analytical methods. Considering the time constraints and the need to stress the interpretative rather than the computational skills, the authors have developed the software package UATSA (University of Arizona Time Series Analysis) that incorporates many analytical tools commonly used in time series analysis in an organized and sequential manner: exploratory statistics, markovian processes, univariate and multivariate analyses (ARMA models), frequency decomposition algorithms, principal components, canonical correlations and cluster analyses are included within the current version of the package. UATSA is a stand-alone executable file compiled in MATLAB® that has been used in courses of time series analysis in hydrology at the University of Arizona and in several workshops offered by the authors between 2004 and 2007. The software aims to easily illustrate the use of algorithms in the synthesis and decomposition of time series, providing a background to the methods and a visual platform that is user friendly and data extensive. The software has evolved through the years, incorporating suggestions made by students to improve its appearance and widen its scope. 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