Time sequence classification method and system based on time sequence two-dimension and width learning

Abstract

The invention discloses a time sequence classification method based on time sequence two-dimension and width learning, and the method comprises the following steps: 1), carrying out the normalization processing of input data, and obtaining X; 2) performing feature extraction on X by using four sub-width learning systems and width learning to obtain an input U of the deep cascade width learning system; and 3) classifying the input data U by the deep cascade width learning system. According to the method, width learning and time sequence two-dimensionalization are combined, a feature extraction mode is provided, the features of the time sequence can be fully extracted, the dimensionality reduction effect can be achieved, and data more suitable for a deep cascade width learning system can be extracted. After feature extraction is completed, a deep cascade width learning system is used for classification, so that the time cost and the calculation cost can be remarkably reduced. The invention further provides a time sequence classification system based on time sequence two-dimension and width learning.

Description

Technical field[0001]The present invention belongs to the field of time sequence data classification, in particular, is a time series classification method and system based on timing 2rd and width learning.Background technique[0002]Time series data is a collection of observation values ​​that observed in chronological order. It is a class of important data objects in sequence data, and the characteristics of time series data include large data volume and high dimensionality. Classification and prediction is a classic problem in the field of data mining, but due to the complexity of time series data, in the past studies, time series data classification prediction has become a special challenge in classification research. A large number of work is committed to the development and improvement of time series prediction, including from a single variable to a multivariate, from offline to online, from a short sequence to a long sequence, etc.[0003]Different tasks use different technical p...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a time series classification method and system based on time series two-dimensionalization and width learning in order to alleviate the difficulty of time series data classification and the time cost and high calculation cost of deep learning classification methods

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