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This paper introduces a novel generic method aimed at predicting motion and force information in haptic media. An autoregressive (AR) model is presented for the prediction of both, haptic movement and force. The conditional maximum likelihood technique is utilized in order to accurately estimate the adaptive coefficients of the AR model. Furthermore, the incorporation of concepts from haptic perceptibility, i.e. the Just Noticeable Difference (JND), has been demonstrated to optimize the suggested algorithm, while preserving the immersiveness of the haptic-enabled environment. The proposed technique has also proved to provide accurate prediction results for non-linear haptic movement and force information while simultaneously remaining computationally efficient.

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