Abstract
A New Time Series Similarity Measurement Method Based on Fluctuation Features
Highlights
Time series data come up in a variety of domains [1], including financial data [2], environmental data [3, 4], telecommunication data [5], and medical data [6]
Compared to some traditional time series similarity measurements, the clustering results show that the proposed method can effectively distinguish time series with similar shapes from different classes and get a visible improvement in clustering accuracy in terms of F-Measure
Time series similarity measurement is the basis for time series clustering [15], which is used to calculate the distance of two time series
Summary
Time series data come up in a variety of domains [1], including financial data [2], environmental data [3, 4], telecommunication data [5], and medical data [6]. The similarity measurement based on segmented representation distance, such as Piecewise Linear Approximation (PLA) [21], Piecewise Aggregate Approximation (PAA) [22], and Derivative Segment Approximation (DSA) [23], segments the long time series into several short sequences and uses the features of segmented sequences to represent the original time series. The similarity measurement based on model distance includes Auto-Regressive Model (AR) [25], AutoRegressive and Moving Average Model (ARMA) [26], and Hidden Markov Model (HMM) [27] This method describes the original time series by solving the appropriate parameter to fit model, and expresses the distance between the parameters as a similarity index. According to the above definition, the extreme points with small changes are filtered to obtain candidate fluctuation points (Fig. 2)
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