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

Prediction method for retail sales volume of gas station convenience store

A forecasting method and convenience store technology, applied in marketing, commerce, instruments, etc., can solve problems such as less research work and immature theories

Pending Publication Date: 2019-09-10
KUNMING UNIV OF SCI & TECH
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In view of the fact that gas station convenience stores are a new business, there are few domestic research works, and the relevant theories are not yet mature, so how to accurately predict its retail sales is facing many difficulties

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
  • Prediction method for retail sales volume of gas station convenience store
  • Prediction method for retail sales volume of gas station convenience store
  • Prediction method for retail sales volume of gas station convenience store

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0086] Embodiment 1: A method for predicting retail sales of gas station convenience stores, the specific steps are as follows:

[0087] (1) Establish an unbiased gray prediction GM(1,1) model:

[0088] (1.1) First assume that the original data sequence is

[0089] x (0) =(X (0) (1),X (0) (2),...,X (0) (n)) (1)

[0090] (1.2) Then use the accumulation of the original data sequence to generate a new sequence as:

[0091] x (1) =(X (1) (1),X (1) (2),...,X (1) (n)) (2)

[0092] in,

[0093] (1.3) Calculate parameter values:

[0094] Get the parameter column by the method of least squares

[0095]

[0096] In the formula:

[0097]

[0098] (1.4) Establish an unbiased GM(1,1) prediction model:

[0099] Suppose the original data sequence satisfies:

[0100]

[0101] Where: k=1,2,...,n, a 1 ,b 1 is the undetermined coefficient;

[0102] Each x of formula (4) (0) (k) Carry out an accumulation to get:

[0103]

[0104] Substituting equations (4) and (...

Embodiment 2

[0162] Example 2: Select the sales data of CNPC Kunming Hongwa Station Convenience Store from the first quarter of 2015 to the third quarter of 2018 as a data sample, and establish an unbiased gray PSO-Markov prediction model according to the method in this paper. The parameters of PSO optimization are set as follows: the number of particle swarms m=500, the maximum number of iterations iter max = 200, particle velocity v∈[-0.1,0.1], particle position y∈[-2,2], c 1 = c 2 =2. The whitening weight function of different states obtained by improving the PSO-Markov algorithm is: a 1 =0.8030,a 2 =0.1619,a 3 = 0.0015.

[0163] The PMGM(1,1) prediction model based on PSO-Markov optimization was compared with the Markov-optimized MGM(1,1) prediction model and the unbiased gray GM(1,1) prediction model. Taking gas station convenience stores from the first quarter of 2015 to the third quarter of 2018 as the known data, the above modeling methods are used to build models to predict ...

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 prediction method for retail sales volume of a gas station convenience store, and the method comprises the steps od firstly, building an unbiased GM (1, 1) prediction model based on a grey system theory, and eliminating the inherent deviation of a conventional GM (1, 1) prediction model; secondly, correcting the relative residual error of the unbiased GM (1, 1) predictionmodel by using the Markov theory, wherein the model can better reflect the fluctuation characteristics of the data, and finally, whitening the parameters of the unbiased GM (1, 1)-Markov prediction model gray interval by using an improved particle swarm optimization algorithm, and obtaining the unbiased grey PSO-Markov prediction model. According to the model, the precision of the prediction model can be improved, and the model can be used for the commodity sales prediction of convenience stores of the gas stations and provides a basis for operation decisions of enterprises.

Description

technical field [0001] The invention relates to a method for predicting retail sales of gas station convenience stores, belonging to the field of retail sales of gas station convenience stores. Background technique [0002] With the continuous deepening of reform and development, the domestic refined oil retail market will relax access conditions, and large foreign oil product companies will continue to impact the Chinese market. The profits of oil product operations will continue to shrink, and the risks and difficulties of their operations will increase. In this context, the two major domestic oil product companies (CNPC and Sinopec) are actively expanding their business, learning advanced business concepts from Europe and the United States, vigorously developing the retail business of gas station convenience stores, and ushering in new development opportunities. Accurate forecasting of commodity sales is an important basis for measuring the sales and benefits of gas stati...

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): G06Q30/02
CPCG06Q30/0202
Inventor 刘海明南敢黄涤杨光汪长波王金燕
Owner KUNMING UNIV OF SCI & TECH
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