Efficient time sequence segmentation method
A time-series and high-efficiency technology, applied in the fields of instruments, character and pattern recognition, computer components, etc., can solve the problems of decreased representation accuracy, low processing efficiency, low raw data representation accuracy, etc., to achieve efficient processing, efficient time series, The effect of accurately representing time series trend characteristics
Pending Publication Date: 2021-12-31
KUNMING UNIV OF SCI & TECH
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Problems solved by technology
In order to solve the problem of low accuracy of the original data representation by the classic SW method, Keogh et al. proposed a method based on sliding window and top-down strategy (sliding window and bottom-up, SWAB), which combines the online segmentation method And the advantages of offline segmentation method, improve the accuracy of fitting, but there is a problem of low processing efficiency
In order to solve the problem of low processing efficiency of the SW method, Liu et al. proposed a method based on the feasible space window (FSW). The FSW method uses the method of slope calculation to update the upper and lower boundaries in real time as the method of judging the segmentation point. The processing speed has been greatly improved, but it has also led to a significant drop in representation accuracy, making it impossible to provide high-quality representation results for time series big data mining research work
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Embodiment
[0026] Embodiment, an efficient time series segmentation method, refer to Figure 1-4 ,Proceed as follows:
[0027] Step 1. Import the time series, and provide the UCR_Archive data set under the online website from the University of California, Riverside; use MATLAB to analyze the original time series data of the data set, and transfer it to a mat file for subsequent data analysis; the program starts, Import time series mat files into MATLAB.
[0028] Step 2. The preset maximum error threshold is δ, the window length threshold is sw_len, the starting point of the window initialization time series is the left boundary point sw_left of the sliding window, and the preset window length threshold in step 2 is set to 4 to 5 to achieve the optimum .
[0029] Step 3. The single-run FSW algorithm (SFSW function) updates the right boundary point sw_right of the sliding window according to the maximum single-point error threshold δ, where each run only finds the first linear segment an...
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The invention provides an efficient time sequence segmentation method. The trend feature is important information of a time sequence, linear representation of the time sequence is achieved by segmenting the time sequence, and therefore extraction of the trend feature of the time sequence is an important direction in the field of time sequence research. The time sequence segmentation method comprises on-line segmentation and off-line segmentation. The off-line segmentation method has the characteristic of high representation precision. The on-line segmentation method has the characteristic of on-line processing, and can still continue the optimization of the time sequence segmentation method. According to the method, an on-line segmentation method FSW algorithm and an off-line segmentation method BU algorithm are integrated, and a FSWAB method is provided, so that the new method can have the advantages of the two algorithms, and efficient on-line segmentation and accurate representation can be carried out on the time sequence. The inventor carries out empirical analysis by using a UCR_Archive data set provided by a riverside branch of California University, and the result shows that the method can efficiently segment the time sequence online and accurately represent the trend characteristics of the time sequence.
Description
technical field [0001] The invention relates to a time series segmentation method, in particular to an online segmentation and linear representation method of the time series. Background technique [0002] A time series is an ordered collection of data that changes over time and is related to each other. With the advent of the era of big data, the amount and complexity of time series data are increasing day by day, and gradually have the characteristics of big data. Online segmentation and piecewise linear representation of time series are of great significance for big data mining research. [0003] Piecewise linear representation (PLR) is an important method in the approximate representation of time series. It has been widely used because of its simplicity and intuitiveness, high processing efficiency, and the representation results conform to human visual experience. The classic piecewise linear representation method According to its processing method, it can be divided ...
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Login to View More IPC IPC(8): G06K9/62
CPCG06F18/2321
Inventor 杜加础车文刚
Owner KUNMING UNIV OF SCI & TECH