Data prediction method and system and alarming method and system

A data prediction and data technology, applied in the fields of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as inability to apply algorithms, simple algorithms, and inaccurate data.

Active Publication Date: 2015-09-09
SHANGHAI CTRIP COMMERCE CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the present invention is to overcome the disadvantages of some methods in the prior art for predicting website index data that some algorithms are simple, the predicted data is inaccurate, and some algorithms cannot be applied to actual situations, and provide a method with accurate A data prediction method and system for website indicators and an alarm method and system for predicting the data of website indicators and the function of timely alarming when various indicators are abnormal

Method used

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  • Data prediction method and system and alarming method and system
  • Data prediction method and system and alarming method and system
  • Data prediction method and system and alarming method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0091] Such as figure 1 As shown, this embodiment provides a data prediction method for website indicators. From the perspective of prediction itself, this is a complicated process. It is not just determined by the prediction algorithm in the intuitive concept. The data prediction of website indicators includes data Complex issues including data collection, data classification, data correction, data analysis, and predictive algorithms. The data prediction method includes the following steps:

[0092] Step 101. Collect historical data of an indicator in a preset area (such as the Shanghai area), classify these historical data according to time periods (for example, by week), and the historical data in each time period are in order of date Classification.

[0093] Among them, there are roughly 93 indicators to measure the operation of the website, such indicators include page bounce rate, conversion rate, page views, page view rate, browser view rate or main source view rate, ...

Embodiment 2

[0126] The data prediction method of this embodiment includes all the content of the data prediction method in Embodiment 1, and the data prediction system of this embodiment includes all the components and the functions of each component of the data prediction system in Embodiment 1, and on this basis The data prediction method and system of this embodiment increase the technical content and detailed technical content as follows:

[0127] After step 102, it includes the operations of selecting a priori index, calculating the time period index and setting the signal index. These three operations will be described in detail below, and it should be noted that the order of execution of these three operations is not affected by this document. A limitation of the embodiment is that any one of the three operations may be performed first.

[0128] Such as image 3 As shown, select the prior index x t The operation includes the following steps:

[0129] set x i For the historical ...

Embodiment 3

[0181] Next to Example 2, after calculating the forecast data (see steps 101-106 in Example 1 for details), calculate the confidence interval of the forecast data and judge whether the indicator is abnormal according to the confidence interval. The specific steps include:

[0182] Step 107, calculate the standard error corresponding to the forecast date Then the confidence interval of the predicted data is [F-βS e ,F+βS e ]. Among them, W is a positive integer and 1≤w≤W, X w For the historical optimization data for the wth day before the forecast date, F w is the forecast data of the wth day before the forecast date, F is the forecast data, and β is the standard normal distribution quantile, α is the confidence level.

[0183] Step 108 , judging whether the actual data of the indicator on the forecast date is within the confidence interval, if so, output a first message indicating that the indicator is normal, otherwise output a second information indicating that the in...

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Abstract

The invention provides a website index data prediction method and system and an alarming method and system. The data prediction method comprises the following steps: collecting the historical data of an index in a preset area, classifying the historical data according to time periods, and classifying the historical data in each time period according to a sequence of date; dividing the historical data in each time period into standard data and data to be corrected, correcting the data to be corrected according to the standard data, and forming historical optimization data by the corrected data and the standard data; and according to the historical optimization data, calculating the prediction data of the index at a first prediction date through an ARIMAX model. The prediction data of a certain index at a certain date can be accurately predicted, prediction accuracy is obviously improved, an alarm is given when a plurality of indexes used for measuring the operation situation of a website meet certain conditions at the same time, and therefore, users can timely know the operation situation of the website.

Description

technical field [0001] The invention relates to a data prediction of various indexes of a website and a measurement of the operation status of the website, in particular to a data prediction method and a data prediction system of website indexes as well as an alarm method and an alarm system. Background technique [0002] At present, many website monitoring is only aimed at individual indicators, and can only find obvious abnormalities, and the methods used are relatively simple, such as clustering, box plots, etc. In the prior art, many scholars have proposed many prediction methods for the data prediction of website indicators such as page views, such as using the general ARIMA model (Autoregressive Integrated Moving Average Model, differential autoregressive moving average model), but this type of model The influence of factors such as seasons and holidays on data prediction is rarely considered, so the data predicted by the simpler ARIMA model is relatively inaccurate. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G06F17/30
Inventor 卢孔敏周秀凤聂强强康伟华
Owner SHANGHAI CTRIP COMMERCE CO LTD
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