Recently, it has been found that the technique of searching for similar patterns among time series data is very important in a wide range of scientific and business applications. Most of the research works use Euclidean distance as their similarity metric. However, Dynamic Time Warping (DTW) is a more robust distance measure than Euclidean distance in many situations, where sequences may have different lengths or the patterns of which are out of phase in the time axis. Unfortunately, DTW does not satisfy the triangle inequality, so that spatial indexing techniques cannot be applied. In this paper, we present a novel method that supports dynamic time warping for subsequence matching within a collection of sequences. Our method takes full advantage of "sliding window" approach and can handle queries of arbitrary length.