<i>A New Approach to Empirical Mode Decomposition Based on Akima Spline Interpolation Technique</i>

The objective of this research work is to extend the scope empirical mode decomposition (EMD) algorithm, as an efficient tool decompose nonlinear and non-stationary time series. For EMD be widely applicable, extension utilizes both clean noisy data sets. When constructing upper lower envelopes, proposed algorithm Akima spline interpolation technique rather than a cubic spline. called Akima-EMD, which used identify non-informative fluctuations in signal, such noise, outliers, ultra-high frequency components, breakdown chaotic into various components avoiding distortion. It has been shown through synthetic well real-world series analysis that method successfully extracts noise form first IMF from data.