Time sequence segmentation method based on LAC-FLOSS algorithm and IER algorithm

Abstract

The invention discloses a time sequence segmentation method based on an LAC-FLOSS algorithm and an IER algorithm, and relates to a time sequence data segmentation method. The invention provides an improved time sequence segmentation algorithm LAC-FLOSS for limiting arc spanning based on Matrix Profile, the algorithm adds a weight to an arc to form a weighted arc, and then the problem of cross-state sub-sequence mismatching of the arc is solved by setting a matching distance threshold. In addition, the shape features of the CAC sequence are utilized to extract the minimum value from the trough, and then an improved segmentation point extraction algorithm IER is provided. According to the algorithm, the situation that an existing segmentation point extraction algorithm ER uses a window to take segmentation points at non-inflection points can be avoided, the accuracy of segmentation result extraction is improved, and through comparison with the ER algorithm, it is verified that the segmentation point extraction effect of the IER is better than that of the algorithm ER.

Description

technical field [0001] The invention relates to a time series data segmentation method, in particular to a time series segmentation method based on LAC-FLOSS algorithm and IER algorithm. Background technique [0002] Time series segmentation is an important preset step in the field of time series correlation research, which provides a good support for further analysis of time series. Time series segmentation is mainly used in human activity analysis, somatosensory games, trajectory prediction, and anomaly detection of human activity. [0003] At present, the popular time series segmentation technology based on Matrix Profile is to divide continuous actions, which is composed of Fast Low-cost Semantic Segmentation (FLOSS) algorithm and Extract Regimes (ER). Unlike most methods for processing time series segmentation, it is domain-independent and will not be limited to specific domains. It can easily process streaming data and has ready-to-use characteristics, that is, it can...

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Problems solved by technology

[0007] The above technologies usually have the following problems: 1. When the FLOSS algorithm calculates the Matrix Profile, in the process of matching subsequences, arcs cross different states to match the same state, resulting in the loss of a large number of CAC sequences that can be used to extract segmentation points. features, so the segmentation effect is not good

2. When the ER algorithm uses the excluded domain window to extract the minimum value of the CAC sequence, when the window is at a curve with a slope that is constantly increasing or decreasing, it cannot guarantee that the minimum value obtained is the position of the segmentation point, resulting in the extracted There will be a large error between the segmentation result and the real result, and the extraction effect is not good

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