Forecasting of the true satellite carbon monoxide data with ensemble empirical mode decomposition, singular value decomposition and moving average

Abstract

The forecasting of carbon monoxide in the atmosphere is essential as it causes the pollution of the atmosphere and hence severe health problems for humans. This study proposes a time-series prognosis EEMD-SVD-MA technique which incorporates Ensemble Empirical Mode Decomposition, Singular Value Decomposition and Moving Average, to predict the prospects of carbon monoxide data taken from the Indian region. The collected data are non-linear. The technique can be applied for non-stationary and non-linear data. In this approach, there are three levels: EEMD level, SVD level and MA level. The first level deploys EEMD to fragment data series into a limited number of Intrinsic Mode Function (IMF) components along with a residue. To denoise each IMF component, SVD is deployed in the second level. In the third level, each denoised IMF component is predicted by MA. The future values of the original data are obtained by adding all the predicted series of the components. In this study, we proposed two variants of the model: EEMD-SVD-MA(3) and EEMD-SVD-MA(4) and compared the results with other forecasting techniques, namely LSTM (Long Short Term Memory network), EMD-LSTM, EMD-MA, EEMD-MA and CEEMDAN-MA. The results show that the proposed EEMD-SVD-MA model is more efficient than other models.

Keywords:

• Intrinsic mode function
• empirical mode decomposition
• ensemble empirical mode decomposition
• singular value decomposition
• moving average
• long short term memory network

Mathematics Subject Classifications:

• 37M10
• 68T07
• 15A18

Acknowledgments

The author's deep appreciation goes out to NASA's teams for AIRS/AMSU, MODIS and MOPPIT data for tropospheric CO.

Disclosure statement

No potential conflict of interest was reported by the author(s).