Spectral band decomposition combined with nonlinear models: application to indoor formaldehyde concentration forecasting

Abstract

This paper proposes a new approach for forecasting continuous indoor air quality time series and in particular the concentration of a common air pollutant in offices like formaldehyde. Forecasting is achieved through the combination of the spectral band decomposition using fast Fourier transform and nonlinear time series modeling. Two nonlinear models have been tested: a threshold autoregressive (TAR) model and a Chaos dynamics-based modeling. This study shows the benefit of the Fourier decomposition coupled with nonlinear modeling of each extracted component, compared to forecasting applied directly on the raw data. Both TAR and Chaos dynamics models are able to reproduce nonlinearities, with slightly better performance in the case of the second model. These hybrid models provide good performance on forecast time horizon up to 12 h ahead.