Internet of Things time series data prediction method, system and device and storage medium

Abstract

The invention discloses an Internet of Things time series data prediction method, system and device and a storage medium. The Internet of Things time series data prediction method comprises the steps of obtaining a time series data set; smoothing the time sequence data set to generate a first curve; constructing functional time series data according to the first curve; judging whether the function type time sequence data is stable or not, and if yes, performing function type principal component analysis on the function type time sequence data to generate a second curve; and predicting the time series data according to the second curve. According to the method, the time sequence data set is converted into the function type time sequence data with time dependency, so that the problems of large data volume, information loss and complicated change of high-frequency data of the Internet of Things are effectively solved; by performing function type principal component analysis on the function type time series data, the problem that feature extraction of infinite-dimension function type time series data is difficult is solved, good model generalization ability and accurate long-term prediction precision are achieved, and the method can be widely applied to the technical field of the Internet of Things.

Description

technical field [0001] The present application relates to the technical field of the Internet of Things, in particular to a method, system, device and storage medium for predicting time series data of the Internet of Things. Background technique [0002] When the Internet of Things technology is used in transportation, energy, agriculture, environmental monitoring and other fields, it usually needs to collect a large amount of data in real time through various sensing devices, which makes different types of data in different fields show explosive growth. Decision support puts forward higher requirements. On this basis, traditional time series data forecasting methods have been difficult to deal with massive and complex time series data. At the same time, traditional time series data prediction methods mostly use low-frequency data, and fail to fully apply high-frequency data with richer data information. In addition, for the Internet of Things time-series data with extreme...

AI Technical Summary

Problems solved by technology

With the development of the Internet of Things technology, the above-mentioned forecasting techniques have been unable to analyze the increasingly complex time series data of the Internet of Things, and it is difficult to meet the requirements of long-term forecasting

Images