The analysis of time series using data mining techniques can be effective when all targets have their own inherent patterns in a sparse sampling acoustic sensor network where no valid feature of frequency can be extracted. However, both problems of local time shifting and spatial variations should be solved to deploy the time series analysis. This paper presents time-warped similarity measure algorithms in order to solve the two problems through time series, and we propose the IDDC (Improved Derivative DTW-Cosine) algorithm to deliver the optimal result and prove the performance with some experiments. The experimental results show that the object classification accuracy rate of the proposed algorithm outperforms the other time-warped similarity measure algorithms by at least 10.23%. Since this proposed algorithm produces such a satisfactory result with sparse sampling data, it allows us to classify objects with relatively low overhead.