基于小波分析的时间序列数据挖掘模型

A Model Based on Wavelet Analysis for Data Mining on Time Series

论文提出一个基于小波分析的时间序列挖掘模型TSMiner,它支持时间序列数据挖掘的整个过程。该模型由5部分组成:原始数据的可视化、数据预处理、数据约简,模式发现和结果模式可视化。该模型应用小波实现数据的多层次可视化表示、数据约简和多尺度模式发现。它可以帮助用户观察高维数据,理解中间结果和解释发现的模式。

TSMiner,a model for time series visual data mining based on wavelet has been prosposed.The model consists of five components:original data visualization,data preprocess,data reduction,pattern discovery and pattern visualization.By wavelets the model performs hierarchical representation of time series dataset for visualization,data reduction and multi-scale pattern discovery.This model can help users view the high dimensional data,understand the intermediate results,and interpret the discovered patterns.