A processing method for counting maximum continuous increasing times and increasing times of time sequence data

Abstract

The invention discloses a processing method for counting maximum continuous increasing times and increasing times of time sequence data. According to the invention, data sequences are grouped in advance; calculating the maximum continuous increasing and decreasing frequency of each group; storing an intermediate result on a corresponding timestamp according to the initial element, the initial element, the tail element, the continuous increasing and decreasing times starting from the initial element, the continuous increasing and decreasing times starting from the tail element and the element number of each group, and quickly obtaining a calculation result according to a specified timestamp during query; due to the fact that only the calculated intermediate result is stored, memory consumption is reduced, storage efficiency is improved, and due to the fact that the intermediate result is calculated in advance, response speed is extremely high during query. Most importantly, the result continuously changes along with system time movement, and the purpose of smooth movement of the time window can be achieved. The method is applicable to the field of data analysis such as a time sequence data processing technology, and the speed of calculating the maximum continuous increasing and decreasing times of the time sequence data can be remarkably increased.

Description

technical field [0001] The invention is a time-series data processing technology. According to the time stamp in the time-series data, the time-series calculation result of a specified interval is stored on the corresponding time stamp of the interval, thereby providing the capability of time window parallel sliding processing. It is mainly applicable to data analysis fields such as time series data processing technology. Background technique [0002] In the field of time series data analysis and processing, there are many solutions for time window sliding processing, most of which filter the data on an entity to retain the original data, so as to perform adhoc query. Adhoc query is very flexible, but once the entity is too large (data is too dense), the amount of data that needs to be processed each time increases, and the performance will be very low. [0003] Suppose there is an account with the following sequence, ⑦ ⑤⑥ ② Arranged as figure 1 shown. Assuming th...

AI Technical Summary

Problems solved by technology

Because the calculation needs to be repeated based on the original time series every time, the whole calculation process is complex and inefficient. The only advantage is that the calculation is more flexible.

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