关键词: 时间序列分析, 符号聚合近似, 生产线数据分析, 分类学习

Abstract: To solve the problem of losing sequence fluctuation information when analyzing time series data with traditional symbol aggregation approximation methods,a new Symbol Aggregation approximation method of time series combined with Volatility (SAX_VOLA) was proposed.In this method,the volatility index to quantify the fluctuation range and trend information was introduced,and the symbol vector fused with volatility was used to approximate the subsequence.On this Basis,a new distance metric was proposed to measure the similarity of time series and to perform classification learning.The proposed method was tested on several open data sets,and the experimental results showed that the better classification accuracy than traditional symbolic aggregation approximation methods was achieved,especially when times series had obvious local fluctuations or obvious upward and downward trends.

Key words: time series analysis, symbol aggregation approximation, production data analysis, classification learning