Missing data filling based on the spectral analysis and the Long Short- Term Memory network

Abstract

A combined missing data filling approach based on the spectral analysis and the Long Short-Term Memory (LSTM) network is put forward in this paper to solve the data missing problem in wind speed. Firstly, the periodicity of wind speed data is determined by the periodogram and spectral density estimation results. Then two periodicity-related prediction filling strategies named the forward periodic prediction filling and the inverse periodic prediction filling are designed and realized through LSTM networks along with a non-periodicity-related sequence prediction filling strategy called the sequence prediction filling. Finally, the results of the three prediction filling models are combined according to the best weight vector obtained by the parameter optimization algorithm. Error comparison results demonstrate that the proposed approach performs well in wind speed missing data filling.