Method for modeling and recognizing time sequence

Abstract

The invention relates to a method for modeling and recognizing a time sequence, and belongs to the field of machine learning. The method comprises the steps: enabling dynamic data formed by the sequential arrangement of data, collected by a sensor, according to the sequence of the collection time to form L time sequences, and randomly selecting N time sequences (60%-80%) of the L time sequences as a training set, wherein the remaining time sequences serve as a test set; modeling each time sequence in the training set through employing a linear dynamic system, and employing the features of each time sequence in the training set for representation; randomly extracting J time sequence as dictionaries from the training seat to form a dictionary set, learning the optimal feature representation of each time sequence in the dictionary set from the obtained feature representation of each training time sequence in the training set, and calculating the coding coefficient of each training time sequence in the dictionary set; training a support vector machine model through the coding coefficients of the training set, and achieving the recognition of the time sequences. The method greatly reduces the complexity of data representation, and remarkably improves the recognition precision.

Description

technical field [0001] The invention relates to a method for modeling and identifying time series, which belongs to the field of machine learning. Background technique [0002] In recent years, with the development of different sensor sensing technologies such as cameras and force sensors, the speed of data generation and collection is getting faster and faster, and the amount of data storage is also increasing, and most of the data are transmitted and collected in the form of time series. storage. The so-called time series refers to the dynamic data that the data collected by the sensor is arranged in the order of collection time. In the face of massive time series data, pure manpower can no longer effectively analyze it and extract useful feature information. Therefore, how to design efficient data analysis algorithms, organically refine the time series collected by different sensors, obtain effective representations rich in information, and use effective representations...

AI Technical Summary

Problems solved by technology

First of all, different recognition targets correspond to different physical processes, and the natural time lengths of their dynamic data are usually not equal, and processing all data with equal lengths will cause some data distortion; secondly, if the intercepted time length is too short, A lot of useful information will be lost. If the interception time is too long, the dimension of the data block will be too large, which will greatly reduce the operating efficiency of the later recognition algorithm

Images