CN110865928A - Method for realizing capacity prediction based on ARIMA prediction model and gray prediction model - Google Patents
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Abstract
The invention discloses a method for realizing capacity prediction based on an ARIMA prediction model and a gray prediction model, which comprises the following steps: s1: acquiring a historical capacity data time sequence; s2: preprocessing the capacity historical data to obtain a sample time sequence; s3: calculating a discrete coefficient of the capacity historical data, and selecting a prediction model according to the discrete coefficient; s4: establishing a selected prediction model through the sample time sequence, and checking to obtain a qualified prediction model; s5: acquiring the actually used capacity data of the latest period for preprocessing to obtain a predicted data time sequence; s6: the prediction result is obtained by inputting the prediction data time series into S4. According to the method, for sample data with different properties, the ARIMA prediction model and the gray prediction model are combined to predict the capacity, so that the method is more targeted and has strong applicability; the accuracy of the prediction result is ensured, and accurate guidance is provided for operation and maintenance.
Description
Technical Field
The invention relates to a capacity prediction method, in particular to a method for realizing capacity prediction based on an ARIMA prediction model and a gray prediction model.
Background
In an operation and maintenance scene, capacity prediction aiming at indexes such as the utilization rate of a CPU (Central processing Unit) of equipment, the utilization rate of a memory, the utilization rate of a disk and the like is always a difficult point. The service pressure born by equipment of different systems is different, the capacity consumption is dynamic, the discreteness of most capacity index data is large, and the periodic condition of the capacity use cannot be accurately evaluated.
At present, an operation and maintenance team can only use a monitoring tool, such as zabbix, to configure a monitoring item and an alarm trigger of an index needing attention, to monitor the service condition of related capacity in real time, and perform processing such as capacity expansion at the first time when an alarm occurs. Or the capacity is predicted manually, and whether the capacity needs to be expanded or not is judged subjectively according to personal experience of operation and maintenance personnel.
The current method has the following problems:
1. the capacity management is carried out in a monitoring alarm mode, the risk is high, and the online service is easily influenced.
2. The subjectivity of manual prediction is strong, the operation and maintenance capability and experience of operation and maintenance personnel are tested relatively, and no guarantee is provided.
The ARIMA Model is called an Autoregressive integrated moving Average Model (ARIMA), and is a famous Time-series prediction method proposed by bosch (Box) and Jenkins (Jenkins) in the beginning of the 70 s, so the ARIMA Model is also called a Box-Jenkins Model and a bosch-Jenkins method. Wherein ARIMA (p, d, q) is called a differential autoregressive moving average model, AR is autoregressive, and p is an autoregressive term; MA is the moving average, q is the number of terms of the moving average, and d is the number of differences made when the time series becomes stationary. The ARIMA model is a model established by converting a non-stationary time sequence into a stationary time sequence and then regressing a dependent variable only on a hysteresis value of the dependent variable and a current value and a hysteresis value of a random error term. The ARIMA model includes a moving average process (MA), an autoregressive process (AR), an autoregressive moving average process (ARMA), and an ARIMA process depending on whether the original sequence is stationary and the part involved in the regression.
Gray prediction is a method of predicting systems that contain uncertainty. The grey prediction is to identify the degree of dissimilarity of development trends among system factors, namely, to perform correlation analysis, and to perform generation processing on the original data to find the rule of system change, to generate a data sequence with strong regularity, and then to establish a corresponding differential equation model, thereby predicting the condition of future development trends of objects. A gray prediction model is constructed by using a series of quantitative values of the characteristics of a reaction prediction object observed at equal time intervals, and the characteristic quantity of a certain future moment or the time for reaching a certain characteristic quantity is predicted.
Therefore, it is necessary to provide a method for capacity prediction based on ARIMA prediction model and gray prediction model.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for realizing capacity prediction based on an ARIMA prediction model and a gray prediction model, and solves the problem.
The technical scheme adopted by the invention for solving the technical problems is to provide a method for realizing capacity prediction based on an ARIMA prediction model and a gray prediction model, which comprises the following steps: s1: acquiring a historical capacity data time sequence; s2: preprocessing historical capacity data to obtain a sample time sequence; s3: calculating a discrete coefficient of historical capacity data, and selecting a prediction model according to the discrete coefficient; s4: establishing a selected prediction model through the sample time sequence, and checking to obtain a qualified prediction model; s5: acquiring the actually used capacity data of the latest period for preprocessing to obtain a predicted data time sequence; s6: and inputting the time series of the predicted data into the qualified prediction model obtained in the step S4 to obtain a prediction result.
Further, the capacity data includes CPU utilization, memory utilization and disk utilization data; the capacity data is preprocessed by filtering the data to remove burrs by adopting a normal distribution algorithm.
Further, if the discrete coefficient calculated in step S3 is smaller than the set discrete coefficient, the ARIMA prediction model is selected, otherwise, the gray prediction model is selected.
Further, if the ARIMA prediction model is selected in step S3, step S4 specifically includes:
s411: establishing an ARIMA prediction model as ARIMA (p, d, q); wherein AR is autoregressive, MA is moving average, p is the number of autoregressive terms, d is the number of difference times when the time series becomes stationary time series, and q is the number of moving average terms; s412: drawing the sample data time sequence, observing whether the sample data time sequence is a stable time sequence, carrying out d-order differential operation on a non-stable time sequence to convert the non-stable time sequence into a stable time sequence, and obtaining the stable sample time sequence; s413: dividing the stable sample time sequence into a training sample and a test sample according to the time sequence; s414: estimating the autoregressive item number p and the moving average item number q of the ARIMA prediction model by utilizing an autocorrelation function and a partial autocorrelation function based on a training sample, and combining the difference times d when a time sequence becomes a stable time sequence to obtain an initial ARIMA prediction model; s415: the time sequence is regarded as the combination of a linear autocorrelation part Lt and a nonlinear residual Kt, the prediction result obtained by the initial ARIMA prediction model in the step S44 is recorded as Lt, the prediction residual of Lt is Kt, and the ARIMA residual is calculated based on the training sample; s416: if the ARIMA residual errors are subjected to normal distribution with zero mean value and invariable variance, the ARIMA residual errors are adopted for judgment by utilizing a test sample, and the significance test of an initial ARIMA prediction model is carried out; s417: and if the result of the significance test meets the preset standard, taking the initial ARIMA prediction model as a qualified ARIMA prediction model, otherwise, repeating the steps S413-S416, and adjusting the number p of autoregressive terms and the number q of moving average terms until the qualified ARIMA prediction model is obtained.
Further, if the gray prediction model is selected in step S3, step S4 specifically includes: s421: establishing a gray prediction GM (1,1) model as x(0)(k)+αz(1)(k) B, where x (0) ═ x (0) (1), x (0) (2), …, x (0) (n)) is a sample time series; s422: obtaining estimated values of a and b by regression analysis to obtain an initial gray prediction model; s423: the formula for calculating the predicted value by the initial gray prediction model is:
s424: calculating the residual error of the gray prediction model according to the calculated predicted value sequence and the sample time sequence; s425: carrying out posterior difference inspection through the residual error of the gray prediction model and the sample time sequence; s426: and if the result of the posterior difference test meets the preset standard, taking the initial grey prediction model as a qualified grey prediction model, otherwise, repeating the steps S422-S425, and adjusting the estimated values of a and b until the qualified grey prediction model is obtained.
Compared with the prior art, the invention has the following beneficial effects: according to the method for realizing the capacity prediction based on the ARIMA prediction model and the gray prediction model, the capacity prediction is carried out by combining the ARIMA prediction model and the gray prediction model aiming at sample data with different properties, so that the method is more targeted and has strong applicability; and filtering sample data by normal distribution, removing interference data, correcting the ARIMA prediction model and the gray prediction model, ensuring the accuracy of the prediction result and providing accurate guidance for operation and maintenance.
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FIG. 1 is a flow chart of a method for implementing capacity prediction based on an ARIMA prediction model and a gray prediction model in an embodiment of the present invention;
FIG. 2 is a diagram of the prediction results of the ARIMA prediction model in the embodiment of the present invention;
FIG. 3 is a diagram of the prediction result of the gray prediction model according to the embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the figures and examples.
Fig. 1 is a flowchart of a method for implementing capacity prediction based on an ARIMA prediction model and a gray prediction model in the embodiment of the present invention.
Referring to fig. 1, the method for implementing capacity prediction based on ARIMA prediction model and gray prediction model provided by the present invention includes the following steps:
s1: acquiring a historical capacity data time sequence;
s2: preprocessing historical capacity data to obtain a sample time sequence;
s3: calculating a discrete coefficient of historical capacity data, and selecting a prediction model according to the discrete coefficient;
s4: establishing a selected prediction model through the sample time sequence, and checking to obtain a qualified prediction model;
s5: acquiring the actually used capacity data of the latest period for preprocessing to obtain a predicted data time sequence;
s6: and inputting the time series of the predicted data into the qualified prediction model obtained in the step S4 to obtain a prediction result.
Specifically, the capacity data comprises CPU utilization rate, memory utilization rate and disk utilization rate data; the capacity data is preprocessed by filtering the data to remove burrs by adopting a normal distribution algorithm.
According to the method for realizing the capacity prediction based on the ARIMA prediction model and the gray prediction model, the ARIMA prediction model is selected if the discrete coefficient calculated in the step S3 is smaller than the set discrete coefficient, and the gray prediction model is selected if the ARIMA prediction model is not smaller than the set discrete coefficient.
Specifically, in the method for realizing capacity prediction based on the ARIMA prediction model and the gray prediction model provided by the present invention, if the ARIMA prediction model is selected in step S3, step S4 specifically includes:
s411: establishing an ARIMA prediction model as ARIMA (p, d, q); wherein AR is autoregressive, MA is moving average, p is the number of autoregressive terms, d is the number of difference times when the time series becomes stationary time series, and q is the number of moving average terms;
s412: drawing the sample data time sequence, observing whether the sample data time sequence is a stable time sequence, carrying out d-order differential operation on a non-stable time sequence to convert the non-stable time sequence into a stable time sequence, and obtaining the stable sample time sequence;
s413: dividing the stable sample time sequence into a training sample and a test sample according to the time sequence;
s414: estimating the autoregressive item number p and the moving average item number q of the ARIMA prediction model by utilizing an autocorrelation function and a partial autocorrelation function based on a training sample, and combining the difference times d when a time sequence becomes a stable time sequence to obtain an initial ARIMA prediction model;
s415: the time sequence is regarded as the combination of a linear autocorrelation part Lt and a nonlinear residual Kt, the prediction result obtained by the initial ARIMA prediction model in the step S44 is recorded as Lt, the prediction residual of Lt is Kt, and the ARIMA residual is calculated based on the training sample;
s416: if the ARIMA residual errors are subjected to normal distribution with zero mean value and invariable variance, the ARIMA residual errors are adopted for judgment by utilizing a test sample, and the significance test of an initial ARIMA prediction model is carried out;
s417: and if the result of the significance test meets the preset standard, taking the initial ARIMA prediction model as a qualified ARIMA prediction model, otherwise, repeating the steps S413-S416, and adjusting the number p of autoregressive terms and the number q of moving average terms until the qualified ARIMA prediction model is obtained.
Specifically, in the method for realizing capacity prediction based on the ARIMA prediction model and the gray prediction model provided by the present invention, if the gray prediction model is selected in step S3, step S4 specifically includes:
s421: establishing a gray prediction GM (1,1) model as x(0)(k)+αz(1)(k) B, where x (0) ═ x (0) (1), x (0) (2), …, x (0) (n)) is a sample time series;
the solution of the first order differential equation is:
further obtain
The corresponding formula for calculating the predicted value is as follows:
s422: obtaining estimated values of a and b by regression analysis to obtain an initial gray prediction model;
s423: calculating a predicted value through an initial grey prediction model;
s424: calculating the residual error of the gray prediction model according to the calculated predicted value sequence and the sample time sequence;
s425: carrying out posterior difference inspection through the residual error of the gray prediction model and the sample time sequence;
s426: and if the result of the posterior difference test meets the preset standard, taking the initial grey prediction model as a qualified grey prediction model, otherwise, repeating the steps S422-S425, and adjusting the estimated values of a and b until the qualified grey prediction model is obtained.
Referring to fig. 2 and fig. 3, in the method for realizing capacity prediction based on the ARIMA prediction model and the gray prediction model provided by the present invention, for the same sample time sequence, the ARIMA prediction model is adopted under the condition of a higher discrete coefficient, the prediction result is not ideal, and the prediction result of the gray prediction model is more suitable for the actual situation.
In conclusion, the capacity prediction method based on the ARIMA prediction model and the gray prediction model provided by the invention has the advantages that the capacity prediction is carried out by combining the ARIMA prediction model and the gray prediction model aiming at sample data with different properties, so that the method is more targeted and has strong applicability; and filtering sample data by normal distribution, removing interference data, correcting the ARIMA prediction model and the gray prediction model, ensuring the accuracy of the prediction result and providing accurate guidance for operation and maintenance.
Although the present invention has been described with respect to the preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A method for realizing capacity prediction based on an ARIMA prediction model and a gray prediction model is characterized by comprising the following steps:
s1: acquiring a historical capacity data time sequence;
s2: preprocessing historical capacity data to obtain a sample time sequence;
s3: calculating a discrete coefficient of historical capacity data, and selecting a prediction model according to the discrete coefficient;
s4: establishing a selected prediction model through the sample time sequence, and checking to obtain a qualified prediction model;
s5: acquiring the actually used capacity data of the latest period for preprocessing to obtain a predicted data time sequence;
s6: and inputting the time series of the predicted data into the qualified prediction model obtained in the step S4 to obtain a prediction result.
2. The method of claim 1, wherein the capacity data comprises CPU usage, memory usage, and disk usage data; the capacity data is preprocessed by filtering the data to remove burrs by adopting a normal distribution algorithm.
3. The method of claim 1, wherein the ARIMA prediction model is selected if the calculated dispersion coefficient is smaller than the set dispersion coefficient in step S3, and the gray prediction model is selected otherwise.
4. The method according to claim 3, wherein the ARIMA prediction model is selected in step S3, and the step S4 specifically includes:
s411: establishing an ARIMA prediction model as ARIMA (p, d, q); wherein AR is autoregressive, MA is moving average, p is the number of autoregressive terms, d is the number of difference times when the time series becomes stationary time series, and q is the number of moving average terms;
s412: drawing the sample data time sequence, observing whether the sample data time sequence is a stable time sequence, carrying out d-order differential operation on a non-stable time sequence to convert the non-stable time sequence into a stable time sequence, and obtaining the stable sample time sequence;
s413: dividing the stable sample time sequence into a training sample and a test sample according to the time sequence;
s414: estimating the autoregressive item number p and the moving average item number q of the ARIMA prediction model by utilizing an autocorrelation function and a partial autocorrelation function based on a training sample, and combining the difference times d when a time sequence becomes a stable time sequence to obtain an initial ARIMA prediction model;
s415: the time sequence is regarded as the combination of a linear autocorrelation part Lt and a nonlinear residual Kt, the prediction result obtained by the initial ARIMA prediction model in the step S44 is recorded as Lt, the prediction residual of Lt is Kt, and the ARIMA residual is calculated based on the training sample;
s416: if the ARIMA residual errors are subjected to normal distribution with zero mean value and invariable variance, the ARIMA residual errors are adopted for judgment by utilizing a test sample, and the significance test of an initial ARIMA prediction model is carried out;
s417: and if the result of the significance test meets the preset standard, taking the initial ARIMA prediction model as a qualified ARIMA prediction model, otherwise, repeating the steps S413-S416, and adjusting the number p of autoregressive terms and the number q of moving average terms until the qualified ARIMA prediction model is obtained.
5. The method according to claim 3, wherein the capacity prediction is implemented based on an ARIMA prediction model and a gray prediction model, and wherein the gray prediction model is selected in step S3, then step S4 specifically includes:
s421: the gray prediction GM (1,1) model is established as
x(0)(k)+αz(1)(k) B, where x (0) ═ x (0) (1), x (0) (2), …, x (0) (n)) is a sample time series;
s422: obtaining estimated values of a and b by regression analysis to obtain an initial gray prediction model;
s423: the formula for calculating the predicted value by the initial gray prediction model is:
s424: calculating the residual error of the gray prediction model according to the calculated predicted value sequence and the sample time sequence;
s425: carrying out posterior difference inspection through the residual error of the gray prediction model and the sample time sequence;
s426: and if the result of the posterior difference test meets the preset standard, taking the initial grey prediction model as a qualified grey prediction model, otherwise, repeating the steps S422-S425, and adjusting the estimated values of a and b until the qualified grey prediction model is obtained.
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| CN113238714A (en) * | 2021-05-28 | 2021-08-10 | 广东好太太智能家居有限公司 | Disk capacity prediction method and system based on historical monitoring data and storage medium |
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