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In this study, several feature combinations are studied to analyse their relevance for online signature verification. Different time functions associated with the signing process are analysed in order to provide some insight on their actual discriminative power. This analysis could also help forensic handwriting experts (FHEs) to further understand the signatures and the writer's behaviour. Among the different feature combinations analysed, a set of features which seems to be relevant for signature analysis by FHEs is particularly considered. The feasibility of developing a system which could complement the FHEs work is evaluated. Two different approximations of the analysed time functions are proposed, one based on the Legendre polynomials and another based on the wavelet decomposition. The coefficients in these orthogonal series expansions of the time functions are used as features to model them. Two different signature styles are considered, namely, Western and Chinese, of one of the most recent publicly available signature databases. The experimental results are promising, in particular for the features that seem to be relevant for the FHEs, since the obtained verification error rates are comparable with the ones reported in the state-of-the-art over the same datasets.