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Method for forecasting evenly distributed live data

A technology of fluctuating data and uniform distribution, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as not very good results

Inactive Publication Date: 2011-08-17
BEIHANG UNIV +1
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Problems solved by technology

However, the gray model also has limitations in application. If the data itself shows a certain growth or decline trend, its prediction accuracy will generally be higher. If the data itself has a certain smoothness, the prediction accuracy will be better
The effect of directly applying the gray model to uniformly distributed fluctuation data prediction is not very good, so we need to find an algorithm with simple calculation and high prediction accuracy to deal with uniformly distributed fluctuation data prediction

Method used

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  • Method for forecasting evenly distributed live data
  • Method for forecasting evenly distributed live data
  • Method for forecasting evenly distributed live data

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Embodiment Construction

[0076] see Figure 12 , a uniformly distributed fluctuation data prediction method of the present invention, the specific steps of the method are as follows:

[0077] Let the original sequence of n uniformly distributed fluctuation data be X (0) ={x (0) (1),x (0) (2),...,x (0) (n)}, use it to predict m data and get Y (0) ={y (0) (1),...y (0) (n),y (0) (n+1),...,y (0) (n+m)}, where x (0) (k), y (0) (k) represent the sequence X respectively (0) , Y (0) The kth data in . And you can also use {y (0) (1),y (0) (2),...y (0) (n)} detect prediction error, {y (0) (n+1),...,y (0) (n+m)} is to predict m data

[0078] Step 1: Go to the mean: For a given original data sequence X (0) , first calculate its mean value u, and then use the sequence X (0) Each number in subtracts the mean value u, that is, by calculating r (0) (k)=x (0) (k)-u (k=1,...,n) to get the sequence R (0) ={r (0) (1), r (0) (2),...,r (0) (n)};

[0079] Step 2: Find large numbers: in sequence R ...

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Abstract

The invention discloses a method for forecasting evenly distributed live data, comprising the following six steps of: step I, removing average value; step II, finding out a large number; step III, processing trend; step IV, forecasting gray GM (group mark) (1, 1); step V, restoring the data; and step VI, evaluating the error. The method is based on the gray GM (1, 1) model, the input data is featured with smooth growth by subjecting to equalization and delta transformation treatment, the forecasting purpose is achieved by means of GM (1, 1), and the data is finally restored to obtain the prediction data. The method for forecasting evenly distributed live data has the advantages that the design is scientific, the calculation is simple, the work capacity is low, the forecast accuracy is high; the method in the technical field of system reliability analysis has good practical value and wide application prospect.

Description

(1) Technical field: [0001] The invention relates to a data prediction method, in particular to a uniformly distributed fluctuating data prediction method. It is used for evenly distributed fluctuation data prediction in the industry, and belongs to the technical field of system reliability analysis. (two) background technology: [0002] In daily production and life, we encounter many data that are evenly distributed and fluctuate. For example, the vibration signal of the cylinder head of an internal combustion engine is fluctuating data, which can effectively reflect the operating status of the internal combustion engine. If such data can be estimated in advance, it is of great significance to our production and life to provide a basis for early detection of problems. [0003] At present, although there are many methods in data prediction, the prediction of fluctuation data suitable for uniform distribution is blank. For example, although the curve fitting method is simpl...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 冀利刚张叔农周梦张红陈本刚冯畅
Owner BEIHANG UNIV
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