Handwriting movement includes a lot of intelligence such as identity, personality, tacit or explicit skills and so on. Especially, identity authentication by biometric technologies is currently gaining popularity over traditional password based security systems. Online handwritten signature verification is a long time candidate in this area of research. In the present paper handwritten signature is considered as a behavioral biometric attribute produced by the dynamics of human hand movement. We propose an approach for improving verification accuracy from the analysis of reconstruction of the dynamics from multi-dimensional time series generated from online handwritten signature.The approach contains the proposal of a new similarity measure cross translation error (CTE) to measure similarity of local dynamics between two time series and an integration of the proposed measure with conventional Dynamic Time Warping (DTW) which belongs to global dynamics measure. The simulation results with a small scale generated data set and the benchmark data set used in Signature Verification Contest (SVC) 2004 show that the proposed measure is effective in detecting individuality from handwritten time series compared to the popular DTW based measures. The integrated approach is also promising for increasing verification accuracy.