A method for complement labeled time series data

Abstract

A method for complete time series data with label is disclosed and it is mainly used to solve the problem of time series data losing for a whole consecutive period in real scene, The core idea of themethod includes two aspects. Firstly, the low-dimensional time series is organized into high-dimensional form by using Hankel matrix technology, and the high-order time dependency relation is introduced. On this basis, the missing data is filled by using matrix decomposition method, thus the problem of data loss is effectively overcome. Secondly, the label information is modeled in the whole framework of the algorithm, and the label information is used to support the whole process of data complement, so that the complemented data is more in line with the real scene. By reasonably utilizing theideas of the above two aspects, the method proposed by the invention can obtain a better complement effect in a real time series data missing scene; At the same time, the method is interpretable, andcan be extended on the basis of the method, so it can be used in various real scenes effectively.

Description

technical field [0001] The invention relates to a computer application method for data collection and transmission of time series, in particular to a method for completing time series data with labels. Background technique [0002] With the continuous development of computer intelligent perception technology, computing power and storage technology, a huge amount of data can be obtained every day, and there is a lot of knowledge in these data that is worth mining. Time-series data is a collection of observations made in chronological order, which is widely used in many different kinds of applications, such as: behavior capture, sensor networks, weather forecasting, financial market modeling, etc. For time series data, common analysis and processing tasks include prediction / regression, outlier detection, pattern recognition, etc., but these tasks are usually based on complete data. [0003] However, in real scenarios, due to the inevitable data loss caused by equipment perfor...

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Problems solved by technology

However, this method does not work well in the case of sparse data, because the initial values ​​​​filled with simple methods may be misleading to the subsequent optimization process.

In addition, for related work based on matrix decomposition, due to the loss of the entire column of the original data, this will cause the decomposed matrix to be unable to learn in the corresponding column.

[0005] 2) The influence of external factors on the time series cannot be expressed

However, in real scenarios, due to the influence of external factors, time series data is often uncertain. Therefore, when dealing with data loss in a continuous period, especially when the data loss spans a long period of time, related work cannot be calculated. get the actual value

[0006] It is very common in real-world scenarios that time series data is missing for a continuous period of time. However, existing related methods cannot achieve good results in dealing with this problem.

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