Reconstruction of Missing Data in Synthetic Time Series Using EMD
نویسندگان
چکیده
The paper presents a novel method for reconstruction of missing data in time series. The method is based on the decomposition of known parts of time series into monocomponents (Intrinsic Mode Functions, IMF) using Empirical Mode Decomposition (EMD), construction of prediction models for each IMF using known parts of times series and their composition using weighted average. We demonstrate the efficiency of the proposed approach using a synthetic time series data.
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