Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Cosine function and power function combined transformation-based grey prediction model method for initial sequences

A technology of gray forecasting model and initial sequence, which is applied in forecasting, data processing applications, instruments, etc., and can solve the problems that the accuracy of initial sequence prediction is greatly affected

Inactive Publication Date: 2018-04-17
HUAIYIN INSTITUTE OF TECHNOLOGY
View PDF1 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, the classic GM(1,1) model has many defects. According to the survey, the smoothness of the initial sequence has a great influence on the prediction accuracy.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Cosine function and power function combined transformation-based grey prediction model method for initial sequences
  • Cosine function and power function combined transformation-based grey prediction model method for initial sequences
  • Cosine function and power function combined transformation-based grey prediction model method for initial sequences

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0092] The present invention will be specifically introduced below in conjunction with the accompanying drawings and specific embodiments.

[0093] The initial sequence is based on the gray prediction model method of cosine function and power function combined transformation, including the following steps:

[0094] Step 1. Select the original data sequence used by the prediction model according to the prediction target. The original data sequence

[0095] The column is a set of non-negative increasing number data series, denoted as X (0) ,

[0096] Let the original data sequence be:

[0097] x (0) ={x (0) (1),···,x (0) (n)},

[0098] where x (0) (i)>0i=1,...,n;

[0099] Step 2, for the initial sequence X (0) Perform preprocessing to control the range of all data within the interval range, denoted as F (0) , the preprocessing formula is as follows:

[0100]

[0101] where x (0) (k) is the kth value in the initial sequence, m and M are two control parameters res...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a cosine function and power function combined transformation-based grey prediction model method for initial sequences. The method comprises the following steps of: 1, generating an original data sequence; 2, preprocessing an initial sequence; 3, carrying out cosine function and power function combined transformation on the preprocessed sequence; 4, generating a cumulative sequence; 5, carrying out grey prediction; 6, determining a response function; 7, restoring a prediction value; 8, calculating a relative error; 9, selecting a minimum relative error value; and 10, carrying out residual correction on a minimum relative error value sequence to obtain a corrected sequence. The method is capable of being effectively adapted to various data change types and effectivelyimproving prediction precision of models.

Description

technical field [0001] The present invention relates to the technical field of data prediction, in particular to an initial sequence based on Gray forecasting model method for function transformation. Background technique [0002] The scale and growth rate of water transport volume are important indicators for the development of water transport. It is related to how to scientifically plan the layout of ports, waterways, fleets, human resources, ships and port industries. Due to the advantages of simple prediction and high precision, gray prediction has been widely used in waterway transportation prediction. [0003] However, the classic GM (1, 1) model has many defects. According to investigations, the smoothness of the initial sequence has a great influence on the prediction accuracy. The present invention improves the existing method, integrates the cosine function and the power function into the transformation function, and proposes an initial sequence based on A gra...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06Q10/04G06Q50/30
CPCG06Q10/04G06Q50/40
Inventor 包旭张山华周君李耘常绿夏晶晶朱胜雪单珏
Owner HUAIYIN INSTITUTE OF TECHNOLOGY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com