Method and system for predicting flow of self-adaptive differential auto-regression moving average model

Abstract

The invention discloses a method and a system for predicting a flow of a self-adaptive differential auto-regression moving average model. The method and the system are used for causing a model to be more fit with a data trend of a present flow. The technical scheme comprises the following steps: utilizing an ARIMA (Autoregressive Integrated Moving Average) model to forecast the flow, and alarming when a practical value is deviated from a predicted confidence interval; while alarming, starting an alternative plan to monitor a flow data, for preventing an abnormal data from entering into ARIMA model prediction; and when the ARIMA model normally runs, judging if a parameter of the ARIMA model is still suitable in real time, and if not, automatically relearning and acquiring a new model parameter by relearning, thereby promoting the accuracy for model prediction.

Description

technical field [0001] The present invention relates to a real-time monitoring technology of flow in a computer room, in particular to a method and a system for predicting flow by using a differential autoregressive integrated moving average model (AutoRegressiveIntegratedMovingAverageModel, abbreviated as ARIMA model). Background technique [0002] The basic idea of ​​the ARIMA model is to regard the data sequence formed by the forecast object over time as a random sequence, and use a certain mathematical model to approximate this sequence. This model, once identified, can predict future values ​​from the past and present values ​​of the time series. [0003] The basic procedure of ARIMA model forecasting is as follows: figure 1 Shown: [0004] 1. According to the scatter diagram, autocorrelation function and partial autocorrelation function diagram of the time series, the variance, trend and seasonal variation law of the ACF unit root are tested to identify the stationar...

AI Technical Summary

Problems solved by technology

[0010] When monitoring the traffic in the computer room and making real-time predictions, there are two disadvantages in simply looking for a fixed model to predict and monitor: First, as time goes by, the distribution of data may change due to external factors, so the model will not be correct at this time. Second, when the model monitors abnormalities, the current abnormal data is dirty data and cannot be brought into the model for fitting. At this time, it is impossible to predict and analyze the flow value of the next time period.

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