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System adaptive prediction method

A forecasting method and self-adaptive technology, applied in forecasting, data processing applications, marketing, etc., can solve problems such as high sample requirements and lack of versatility

Pending Publication Date: 2019-05-14
FUDAN UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] Intermittent demand is a common feature in aviation material demand. For intermittent demand, the main forecasting methods include weighted moving average method, exponentially weighted moving average method, Croston method, Bootstrap method, gray prediction method and Bayesian method. Among them, the Bayesian method has high requirements on samples and is not universal

Method used

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Embodiment Construction

[0057] The realization program of the method of the present invention adopts Win10 system, python virtual environment.

[0058] The following is the program implementation code (part): including: forecasting of Class A aviation materials, forecasting of Class A aviation materials, forecasting of Class A aviation materials, forecasting of Class A aviation materials; moving average method, exponential smoothing method, etc.

[0059] class material_demand(object):

[0060] @classmethod

[0061] The following code is the prediction method for Class A aviation materials

[0062] def cal_A(self, n, FL, FH, MURR, TAT, alpha, IQ, RQ, SIT):

[0063] RN = n * FL * FH * MURR # From the above parameters, the average replacement number RN per unit time (day) can be calculated

[0064] L = RN * TAT

[0065] K = 0

[0066] flag = math.exp(-L)

[0067] while flag < alpha:

[0068] K = K + 1

[0069] flag += cal(K,L) * math.exp(-L)

[0070] Q = K - IQ + RQ - SIT

[0071] return Q

...

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Abstract

The invention belongs to the technical field of demand prediction, and particularly relates to a system self-adaptive prediction method. A result is predicted by utilizing a plurality of prediction models, and the method comprises the following specific steps: sorting prediction data; Predicting the data by adopting different prediction models; Comparing different prediction results; Selecting a prediction model; Wherein the prediction model comprises a moving average method, an exponential smoothing method, a reliability life calculation prediction method, an intermittent demand prediction method and a Croton prediction method. Experiments show that the optimal pricing scheme can be effectively analyzed on historical data. The method can be applied to various fields of making pricing strategies of aerospace materials, medical instrument materials and valuable instrument materials and the like.

Description

technical field [0001] The invention belongs to the technical field of demand forecasting, and in particular relates to a forecasting method through system self-adaptation. Background technique [0002] Demand forecasting is a complicated work process. It is necessary to master changes in the amount of support tasks, past and present statistical data, product quality, market conditions and various data, and to give full play to the subjective initiative of aviation material staff to make predictions about the future state. As expected. Aviation materials demand forecasting work must be carried out in a planned and step-by-step manner, without repetition, without omission, and in an orderly manner, so as to improve work efficiency and quality. Generally, it can be divided into the following steps: [0003] The first step is to determine the forecast object. Due to the different forecast objects, time, and scope, the analysis methods used in aviation material demand forecas...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q30/02
Inventor 刘宏刚杨卫东李洋
Owner FUDAN UNIV
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