Service index prediction method and device

Abstract

The invention relates to the technical field of computers, and particularly relates to a service index prediction method and device. The method includes: obtaining a historical service indicator sequence, wherein the historical service index sequence represents service indexes arranged according to a continuous unit time sequence, the unit time is corresponding set fluctuation time when each service index is in fluctuation change, each service index corresponding to the set fluctuation time is in periodic and trend change according to duration time, and the duration time is greater than the set fluctuation time; determining a span difference value between a target moment and a historical reference moment, wherein the historical reference moment is determined from time corresponding to thehistorical service index sequence; and predicting the service index of the target moment based on a cubic exponential smoothing model according to the span difference value, the historical service index sequence and each smoothing parameter determined by training. The periodicity and tendency of the data time sequence can be fully mined based on the cubic exponential smoothing model, and the prediction accuracy is improved.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to a method and device for forecasting business indicators. Background technique [0002] At present, in the home-to-home business scenario of a supermarket, shopping mall, etc., the transaction volume per hour is very large, and it shows a growing trend. Downtime will affect the transaction, resulting in a loss of certain transaction volume. For business management evaluation and other needs, it is necessary to be able to accurately It is very necessary to accurately know the transaction amount affected by the downtime, that is, how to effectively and accurately predict the transaction amount affected by the downtime. [0003] In related technologies, the main methods used for transaction volume forecasting are: where x i Indicates the sales of the i-th time unit, N is the total time unit, and usually takes the day granularity as the time unit, but in this way, t...

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Problems solved by technology

[0002] At present, in the home-to-home business scenario of a supermarket, shopping mall, etc., the transaction volume per hour is very large, and it shows a growing trend. Downtime will affect the transaction, resulting in a loss of certain transaction volume. For business management evaluation and other needs, it is necessary to be able to accurately It is very necessary to know the transaction amount affected by the downtime accurately, that is, how to effectively and accurately predict the transaction amount affected by the downtime

[0003] In related technologies, the main methods used for transaction volume forecasting are: where x i Indicates the sales of the i-th time unit, N is the total time unit, and usually takes the day granularity as the time unit, but in this way, the average value is directly used to estimate, and the information considered is relatively simple, especially for the home business The scenario usually changes periodically during the working hours of the day, and the periodicity and other information cannot be integrated, so the accuracy of the prediction results is not very high

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