Author(s)

P. van der Veer, J. Cser, O. Schleider & E. Kerckhoffs

Abstract

The paper presents a method for completing missing values in time series by applying neural network computation. Conventional methods to complete fragmentary time series like linear interpolation are chosen for small amounts of missing values; problems occur when there are larger intervals of missing values. So far such gaps are repaired by estimating techniques that use parameter estimation of mathematical models. However such an approach fails when there is not enough information for calibrating the model or when the model is too simplified for reliably completing the data series. Neural networks with their generalisation and memory properties are predestined for this category of problems. In the proposed method a

Keywords