提出了用小波神经网络（WNN）来定量研究高频金融时间序列“日历效应”，通过比较发现WNN是比弹性傅立叶形式（FFF）回归技术更具优势的方法。
The paper proposes application of Wavelet Neural Network in high-frequency time series calendar effects' study. At last, the paper proves that WNN is better than classical FFF regression.
金融高频数据是一种不等间隔的时间序列，现有的相似性查找技术对高频数据的处理效果不佳。
The existing methods of similarity search are not suitable for high frequency financial data, which is a kind of non-interval time series.
高频时间序列通常是指以每小时、每分钟甚至每秒为频率所采集的金融类数据；
High frequency time series is referred to financial data which is sampled with interval of one hour, one minute even one second.
高频时间序列通常是指以每小时、每分钟甚至每秒为频率所采集的金融类数据；
High frequency time series is referred to financial data which is sampled with interval of one hour, one minute even one second.
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