Contribution of singular spectral analysis to forecasting and anomalies detection of indoors air qualitiy

The high impact of air quality on environmental and human health justifies the increasing research activity regarding its measurement, modelling, forecasting and anomaly detection. Raw data offered by sensors usually makes the mentioned time series disciplines difficult. This is why the application...

Descripción completa

Detalles Bibliográficos
Autores: Espinosa Zapata, Felipe|||0000-0003-1588-0947, Bartolomé Martín, Ana Belén, Villoria, Pablo, Rodríguez Sánchez, María Cristina
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/59835
Acceso en línea:http://hdl.handle.net/10017/59835
https://dx.doi.org/10.3390/s22083054
Access Level:acceso abierto
Palabra clave:Air quality monitoring
Singular Spectral Analysis
Time series modelling
Treepartition modelling
Forecasting
Anomalies detection
Electrónica
id ES_b2c8eb166d55cda4a481dbe10d5521dc
oai_identifier_str oai:ebuah.uah.es:10017/59835
network_acronym_str ES
network_name_str España
repository_id_str
spelling Contribution of singular spectral analysis to forecasting and anomalies detection of indoors air qualitiyEspinosa Zapata, Felipe|||0000-0003-1588-0947Bartolomé Martín, Ana BelénVilloria, PabloRodríguez Sánchez, María CristinaAir quality monitoringSingular Spectral AnalysisTime series modellingTreepartition modellingForecastingAnomalies detectionElectrónicaElectrónicaThe high impact of air quality on environmental and human health justifies the increasing research activity regarding its measurement, modelling, forecasting and anomaly detection. Raw data offered by sensors usually makes the mentioned time series disciplines difficult. This is why the application of techniques to improve time series processing is a challenge. In this work, Singular Spectral Analysis (SSA) is applied to air quality analysis from real recorded data as part of the Help Responder research project. Authors evaluate the benefits of working with SSA processed data instead of raw data for modelling and estimation of the resulting time series. However, what is more relevant is the proposal to detect indoor air quality anomalies based on the analysis of the time derivative SSA signal when the time derivative of the noisy original data is useless. A dual methodology, evaluating level and dynamics of the SSA signal variation, contributes to identifying risk situations derived from air quality degradation.Comunidad de MadridMDPI20222022-04-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/59835https://dx.doi.org/10.3390/s22083054reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/598352026-06-18T11:13:07Z
dc.title.none.fl_str_mv Contribution of singular spectral analysis to forecasting and anomalies detection of indoors air qualitiy
title Contribution of singular spectral analysis to forecasting and anomalies detection of indoors air qualitiy
spellingShingle Contribution of singular spectral analysis to forecasting and anomalies detection of indoors air qualitiy
Espinosa Zapata, Felipe|||0000-0003-1588-0947
Air quality monitoring
Singular Spectral Analysis
Time series modelling
Treepartition modelling
Forecasting
Anomalies detection
Electrónica
Electrónica
title_short Contribution of singular spectral analysis to forecasting and anomalies detection of indoors air qualitiy
title_full Contribution of singular spectral analysis to forecasting and anomalies detection of indoors air qualitiy
title_fullStr Contribution of singular spectral analysis to forecasting and anomalies detection of indoors air qualitiy
title_full_unstemmed Contribution of singular spectral analysis to forecasting and anomalies detection of indoors air qualitiy
title_sort Contribution of singular spectral analysis to forecasting and anomalies detection of indoors air qualitiy
dc.creator.none.fl_str_mv Espinosa Zapata, Felipe|||0000-0003-1588-0947
Bartolomé Martín, Ana Belén
Villoria, Pablo
Rodríguez Sánchez, María Cristina
author Espinosa Zapata, Felipe|||0000-0003-1588-0947
author_facet Espinosa Zapata, Felipe|||0000-0003-1588-0947
Bartolomé Martín, Ana Belén
Villoria, Pablo
Rodríguez Sánchez, María Cristina
author_role author
author2 Bartolomé Martín, Ana Belén
Villoria, Pablo
Rodríguez Sánchez, María Cristina
author2_role author
author
author
dc.subject.none.fl_str_mv Air quality monitoring
Singular Spectral Analysis
Time series modelling
Treepartition modelling
Forecasting
Anomalies detection
Electrónica
Electrónica
topic Air quality monitoring
Singular Spectral Analysis
Time series modelling
Treepartition modelling
Forecasting
Anomalies detection
Electrónica
Electrónica
description The high impact of air quality on environmental and human health justifies the increasing research activity regarding its measurement, modelling, forecasting and anomaly detection. Raw data offered by sensors usually makes the mentioned time series disciplines difficult. This is why the application of techniques to improve time series processing is a challenge. In this work, Singular Spectral Analysis (SSA) is applied to air quality analysis from real recorded data as part of the Help Responder research project. Authors evaluate the benefits of working with SSA processed data instead of raw data for modelling and estimation of the resulting time series. However, what is more relevant is the proposal to detect indoor air quality anomalies based on the analysis of the time derivative SSA signal when the time derivative of the noisy original data is useless. A dual methodology, evaluating level and dynamics of the SSA signal variation, contributes to identifying risk situations derived from air quality degradation.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-04-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/59835
https://dx.doi.org/10.3390/s22083054
url http://hdl.handle.net/10017/59835
https://dx.doi.org/10.3390/s22083054
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869417084170010624
score 15,300719

Similares en LA Referencia