关键词: 流, 时间序列, 相似性, 实时, 大数据

Abstract:

Although the dynamic time warping algorithm is suitable for measuring time sequence similarity, streaming time sequence has a large quantity of sequences, potential infinite length, and requirement for high real-time performance in the big data background, thus facing problems of simple algorithm and complex computation. We propose a new streaming dynamic time sequence algorithm according to the features of streaming time sequence based on the Spark calculation platform, which can calculate the approximate value of dynamic time sequence in real-time, and has controllable error, good stability, and ability of processing big data. Experimental results verify its feasibility and stability.

Key words: stream, time sequence, similarity, real time, big data