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Time sequence data fitting and compressing method

A technology of time series data and compression method, applied in the direction of electrical components, code conversion, etc., can solve the problems of real-time performance of compression and compression

Active Publication Date: 2015-04-22
SHENZHEN INSTITUTE OF INFORMATION TECHNOLOGY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The embodiment of the present invention provides a time series data fitting and compression method, which aims to solve the problem that most of the existing technologies use straight lines for fitting, and most of them are lossy compression methods for one-dimensional data, which cannot directly perform two-dimensional, three-dimensional or Compression of more dimensional data and poor real-time performance of compression

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  • Time sequence data fitting and compressing method

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Embodiment 1

[0033] figure 1 Shows the implementation process of the time series data fitting and compression method provided in the first embodiment of the present invention. This embodiment can be adapted to the compression of any dimensional time series data, and can perform time series linear fitting and time series nonlinear fitting. , Without loss of generality, detailed as follows:

[0034] In step S101, each component of the D-dimensional time series data at time t is represented by the sum of the linear combination of M time basis functions and the fitting error of the component.

[0035] In this embodiment, the D-dimensional time series data at time t {x 1 (t),x 2 (t),...,x D (t)}, each component of t=0,1,2,...,N-1 is represented by the sum of the linear combination of M time basis functions and the fitting error of the component, namely:

[0036] x 1 ( t ) = X k = 0 M - 1 w 1 , k f k ( t ) + e 1 ( t ) ...

Embodiment 2

[0054] figure 2 It shows the implementation process of the time series data fitting and compression method provided in the second embodiment of the present invention. The first embodiment is a batch processing fitting method. The main disadvantages of this method are: 1) The inverse matrix needs to be solved; 2 ). When fitting time series data by segments, it is necessary to constantly test the number of data points fitted by each segment to determine the optimal weight coefficient matrix of the segment. These two shortcomings severely limit the real-time performance of time series data fitting. Therefore, it is necessary to transform the first embodiment into an online time series data fitting method to meet the real-time requirements. This embodiment has excellent real-time performance and can be applied. The data fitting for infinite time series is detailed as follows:

[0055] In step S201, each component of the D-dimensional time series data at time t is represented by the ...

Embodiment 3

[0081] image 3 Shows the implementation process of the time series data fitting and compression method provided in the third embodiment of the present invention. When multiple data points are fitted, although the first and second embodiments can ensure that the sum of squares of the fitting error is the smallest, they cannot guarantee The fitting error of all data points is relatively small. In fact, when the number of fitted data points is large, some points will have large fitting errors. Therefore, in order to improve the fitting accuracy of the data, this embodiment adopts a segmentation method to fit multiple data points. The basic principle of achieving lossy compression is: when the number of data points of the current fitting segment is greater than M When, the output is two items: 1) the number of data points for each fitting segment; 2) the optimal weight coefficient matrix for each fitting segment. Using these two data, the fitted data value of all points in the se...

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Abstract

The invention is suitable for the field of a data fitting and compressing technology, and provides a time sequence data fitting and compressing method. The time sequence data fitting and compressing method comprises the following steps of: expressing each component of D-dimensional time sequence data at moment t by using the sum of a linear combination of M time primary functions and a fitting error of the components; defining the fitting error of the D-dimensional time sequence data at the moment t, and defining a mean fitting error difference quadratic sum epsilon N of N D-dimensional time sequence data; obtaining a mean fitting error quadratic sum epsilon N (W) by using a weight coefficient matrix W as a function according to a vector xt consisting of D-dimensional input data, a vector alpha t consisting of the M primary functions and a weight coefficient matrix W; minimizing the mean fitting error quadratic sum epsilon N (W), so as to obtain an optimal weight coefficient matrix Wopt. According to the time sequence data fitting and compressing method provided by the invention, each component of the D-dimensional time sequence data at the moment t is expressed by using the sum of the linear combination of the M time primary functions and the fitting error of the components, so that no limitation on a dimension of data to be compressed exists, and the dimension of the data can be randomly expanded.

Description

Technical field [0001] The invention belongs to the technical field of data fitting and compression, and in particular relates to a time series data fitting and compression method. Background technique [0002] Time, space, and attributes are the three basic data components of Geographic Information System (GIS) databases. "Space" refers to spatial location data and its derived data. "Attribute" refers to thematic attribute data that has no derivation relationship with spatial location. "Time" refers to the time-varying information of time, space and attribute state. With the continuous in-depth research and application of GIS based on spatial databases in recent years, the information that changes with time has attracted more and more attention. Therefore, the concept of Temporal Geographic Information System (TGIS) has been proposed. The organizational core of temporal GIS is spatio-temporal database, and spatio-temporal data model is the foundation of spatio-temporal databa...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H03M7/30
Inventor 刘志军
Owner SHENZHEN INSTITUTE OF INFORMATION TECHNOLOGY