Prediction of climate nonstationary oscillation processes with empirical mode decomposition

Willingness-to-pay Prediction Based on Empirical Mode Decomposition

Long-term prediction of customer preferences is becoming essential for effective product portfolio design in broad industrial sectors such as automotive, aerospace, consumer electronics, where typical concept-to-release times are long (24-60 months). However, nonlinear and nonstationary evolutions of customer preferences hinder accurate prediction of the futures of customer preferences. This pa...

Empirical mode decomposition with missing values

This paper considers an improvement of empirical mode decomposition (EMD) in the presence of missing data. EMD has been widely used to decompose nonlinear and nonstationary signals into some components according to intrinsic frequency called intrinsic mode functions. However, the conventional EMD may not be efficient when missing values are present. This paper proposes a modified EMD procedure ...

Nonparametric prediction of nonstationary spatio-temporal processes

In spatial statistics often the response variable at a given location and time is observed together with some covariates which are known to influence the response. In several applications the relationship between the response and covariates may be unknown, and to prevent misspecification of the model, a nonparametric approach could be appropriate. In this paper prediction and forecasting of the...

Modeling and Prediction of Nonstationary Spatial Processes

Weighted sliding Empirical Mode Decomposition

The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely data-driven manner. EMD adaptively and locally ...