Executing telepresence operations across networks poses a number of challenges. In particular, the transfer of certain data types requires high sampling rates, such as haptic and velocity data. This paper presents a two-step approach which reduces the total amount of data sent across a network, while attempting to reconstruct the original data series. The first step utilises a deadband approach to eliminate the transfer of redundant data. In this sense, redundant refers to the fact that the data will be undetectable or of no consequence at the receiving end. The second step is a prediction implementation to best reconstruct the reduced series to its original form. This combined deadband and prediction approach is applied to velocity data extracted from a telepresence scenario, resulting in an 86% reduction in the amount of data transferred, and good data reconstruction properties.