一种基于DTW的新型故事时间序列相似性度量方法
Similarity Measurement Method Based on DTW for Stock Time Series

现有时间序列相似性度量方法在进行股市序列相似性分析时，通常忽略成交量等其他重要因素对股价的影响，从而导致序列聚类、分类不精确。针对这一问题， 本文提出了新的股市时间序列相似性度量方法。该方法在动态时间弯曲算法的基础上，通过引进时间衰竭因子，并结合成交量因素，给出了股市序列的最终度量公式。为了证明提出方法的可行性和有效性，本文实验部分通过选取家电等三个行业中的股票数据进行测试。实验结果表明，基于动态时间弯曲（Dynamic time warping，DTW）的新型股市时间序列相似性度量方法能够在保持股票序列形态特征的基础上，较好地解决股市技术分析中量价关系问题，从而更有效地应用于股市技术分析里关于模式发现等领域。
The existing similarity measurement methods for stock time series always ignore the trading volume and other important factors influencing the stock price. This phenomenon results in inaccuracy when clustering and classifying the series. To solve the problem, a new similarity measurement method for stock time series is proposed. The method which is based on dynamic time warping(DTW） introduces time-exhaustion factor and trading volume factor, and puts forward the ultimate similarity measurement formula for stock time series. To prove the feasibility and validity of the method, the stock time series in the household appliances and two others in the experiment of this paper are tested. The test result indicates that the new similarity measurement method based on DTW can maintain the shape features of stock series. On this basis, the method can solves the problem of price-volume relationship in the stock technical analysis well. Thus the method can be applied to pattern discovery and other fields in the stock technical analysis for more effective results.