Contribution of singular spectral analysis to forecasting and anomalies detection of indoors air qualitiy
Authors
Espinosa Zapata, Felipe; Bartolomé Martín, Ana Belén; Villoria, Pablo; Rodríguez Sánchez, María CristinaIdentifiers
Permanent link (URI): http://hdl.handle.net/10017/59835DOI: 10.3390/s22083054
ISSN: 1424-8220
Publisher
MDPI
Date
2022-04-01Funders
Comunidad de Madrid
Bibliographic citation
Espinosa, F.; Bartolomé, A.B.; Hernández, P.V.; Rodriguez-Sanchez, M.C. Contribution of Singular Spectral Analysis to Forecasting and Anomalies Detection of Indoors Air Quality. Sensors 2022, 22, 3054.
Keywords
Air quality monitoring
Singular Spectral Analysis
Time series modelling
Treepartition modelling
Forecasting
Anomalies detection
Project
M2184
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.3390/s22083054Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2022 The authors
Access rights
info:eu-repo/semantics/openAccess
Abstract
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.
Files in this item
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Contribution_of_Sensors_2022.pdf | 2.865Mb |
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Contribution_of_Sensors_2022.pdf | 2.865Mb |
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