Construction method of time sequence causal relationship graph

Abstract

The invention discloses a method for constructing a time sequence causal relationship graph, and the method comprises the steps: calculating a first time lag value of each time sequence and a second time lag value between every two time sequences based on a plurality of time sequences; a direct lag dependent variable and an initial connection graph for each time sequence are determined. And judging whether a causal relationship exists between the time sequences corresponding to every two mutually connected nodes in the initial connection graph or not by utilizing a conditional independence criterion, thereby obtaining an intermediate connection graph. And after determining the direction of the undirected edge between the time sequences with the causal lagging relationship in the intermediate connection graph, checking whether the undirected edge between every two time sequences at the current moment actually exists or not by using the conditional independence criterion again, and obtaining a final time sequence causal relationship graph. According to the scheme, every two time sequences are fitted through the first time lag value to obtain the residual sequence, and the second time lag value is calculated by using the residual sequence, so that the accuracy of causal relationship judgment can be improved.

Description

technical field [0001] The application relates to the technical field of causal network structure learning, in particular to a method for constructing a time series causal relationship graph. Background technique [0002] With the increasing use of computers, the requirements for the safe operation of software systems are getting higher and higher. At this stage, the root cause location can be used to determine which index data in the software system is abnormal, that is, the abnormality of time series will lead to software abnormality. happened. Therefore, the construction of the causality diagram for the time series can locate the fault of the software system. In recent decades, large-scale time series data has exploded, and researchers have a wealth of data warehouses, while computing power is also growing, which opens up new ways for time series causal reasoning. [0003] In the existing methods for constructing causality of time series, due to the autocorrelation of t...

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Problems solved by technology

[0004] In view of this, this application provides a method for constructing a time series causal relationship graph, which is used to solve a certain degree of misjudgment when calculating the causal relationship between each two time series due to the autocorrelation of time series , a defect that reduces the accuracy of causality construction

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