Time sequence data processing method and device

Abstract

The invention discloses a time sequence data processing method and device, and the method comprises the steps: obtaining time sequence data, and enabling each piece of data in a time sequence to be avalue of a time point; calculating fluctuation energy E (n) = V (n)-V (n-1) at each time point in sequence, wherein E (n) is the fluctuation energy of the current time point, V (n) is the value of thecurrent time point, and V (n-1) is the value of the previous time point; circularly traversing each time point in the time sequence, and determining the state of the time point according to the fluctuation energy of each time point, with the state comprising a temporary state and a stable state; taking the stable state of the time point as the state mode of the time point; wherein the state modecomprises a stationary mode, a rising mode and a falling mode. BSegmentation of the time sequence data and identification of the state mode can be simply and accurately realized.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a time series data processing method and device. Background technique [0002] Time-series data refers to data collected in chronological order. This type of data reflects the state or degree of a certain thing, phenomenon, etc. over time. It can be intuitively expressed as a time-series waveform in a plane, such as an electrocardiogram (ECG ), electroencephalogram (EEG), current and voltage signals in manufacturing, K-lines for stock trading, time-domain waveforms of speech signals, etc. In the analysis and application of such data, it is usually necessary to segment the waveform and classify the segmented waveforms to provide corresponding information for various applications. [0003] In the existing technology, there are many ways to segment time series data, but most of the existing technologies have various limitations or defects, such as: high computational complexity, inac...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, there are many ways to segment time series data, but most of the existing technologies have various limitations or defects, such as: high computational complexity, inaccurate segmentation in complex scenes, and inapplicability to Real-time computing, highly affected by noisy data, strong assumptions about the distribution or source of data, supervised learning requires training data and labels, or is only applicable to a signal with a specific graphic pattern, etc.

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