On reconstructing high derivatives of noisy time-series with confidence intervals

Abstract

Reconstructing high derivatives of noisy measurements is an important step in many control, identification and diagnosis problems. In this paper, a heuristic is proposed to address this challenging issue. The framework is based on a dictionary of identified models indexed by the bandwidth, the noise level and the required degrees of derivation. Each model in the dictionary is identified via cross-validation using tailored learning data. It is also shown that the proposed approach provides heuristically defined confidence intervals on the resulting estimation. The performance of the framework is compared to the state-of-the-art available algorithms showing noticeably higher accuracy. Although the results are shown for up to the 4-th derivative, higher derivation orders can be used with comparable results.