Air Quality Prediction Based on Singular Spectrum Analysis and Artificial Neural Networks

Singular spectrum analysis is a powerful nonparametric technique used to decompose the original time series into a set of components that can be interpreted as trend, seasonal, and noise. For their part, neural networks are a family of information-processing techniques capable of approximating highl...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Switzerland), 2024-12, Vol.26 (12), p.1062
Main Authors: López-Gonzales, Javier Linkolk, Salas, Rodrigo, Velandia, Daira, Canas Rodrigues, Paulo
Format: Article
Language:English
Subjects:
Accuracy
Air pollution
Air quality
Algorithms
Analysis
Artificial neural networks
Deep learning
Fuzzy logic
Geospatial data
hybrid method
Neural networks
Noise prediction
Outdoor air quality
Pollutants
Recurrent neural networks
Signal processing
singular spectrum analysis
Spectrum analysis
Statistical methods
Time series
Variables
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Summary:Singular spectrum analysis is a powerful nonparametric technique used to decompose the original time series into a set of components that can be interpreted as trend, seasonal, and noise. For their part, neural networks are a family of information-processing techniques capable of approximating highly nonlinear functions. This study proposes to improve the precision in the prediction of air quality. For this purpose, a hybrid adaptation is considered. It is based on an integration of the singular spectrum analysis and the recurrent neural network long short-term memory; the SSA is applied to the original time series to split signal and noise, which are then predicted separately and added together to obtain the final forecasts. This hybrid method provided better performance when compared with other methods.
ISSN:1099-4300
1099-4300
DOI:10.3390/e26121062