The completion of missing values is a prevalent problem in many domains of pattern recognition and signal processing. Analyzing data with incompleteness may lead to a loss of power and unreliable results, especially for large missing sub-sequence(s). Therefore, the aim of this paper is to introduce a new approach for filling successive missing values in low/un-correlated multivariate time series, that is to manage a high level of uncertainty. In this way, we propose to use a novel fuzzy weighting-based similarity measure. The proposed method consists of two main steps. Firstly, for each incomplete signal, the data before a gap and the data after this gap are considered as two separated reference time series with their respective query windows Qb and Qa. We then find the most similar sub-sequence (Qbs) to the sub-sequence before this gap Qb and the most similar one (Qas) to the sub-sequence after the gap Qa. To find these similar windows, we build a new similarity measure based on fuzzy grades of basic similarity measures and on fuzzy logic rules. Finally, we fill in the gap with average values of the window following Qbs and the one preceding Qas. The experimental results have demonstrated that the proposed approach outperforms the state-of-theart methods in case of multivariate time series having low/non-correlated data but effective information on each signal.