Optimum weighted composite prediction method for shipment amount of manufacturing industry

A technology of weighted combination and forecasting method, applied in forecasting, manufacturing computing system, data processing application, etc., can solve the problem of low forecasting accuracy of shipment forecasting model, and achieve the effect of clear thinking, high practicability and simple calculation

Inactive Publication Date: 2016-08-24
SHANDONG UNIV
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  • Application Information

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Problems solved by technology

[0004] Aiming at the problem that the prediction accuracy of the existing manufacturing shipments forecast model is not high, the present inve...

Method used

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  • Optimum weighted composite prediction method for shipment amount of manufacturing industry
  • Optimum weighted composite prediction method for shipment amount of manufacturing industry
  • Optimum weighted composite prediction method for shipment amount of manufacturing industry

Examples

Experimental program
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Effect test

Embodiment 1

[0041] Such as Figure 1-3 shown.

[0042] An optimal weighted combined forecasting method for manufacturing shipments, comprising the following steps:

[0043] S1: Obtain the original monthly data of all manufacturing shipments from the R environment, and divide it into a training set and a test set to form a time series of shipments:

[0044] Obtain the monthly data of all manufacturing shipments from February 1992 to June 2015 from the website of the US Department of Commerce, and divide it into a training set and a test set. The training set data is from February 1992 to March 2015 , the test set data is from April 2015 to June 2015;

[0045] S2: Establish the integrated autoregressive moving average model ARIMA (AutoregressiveIntegrated Moving Average Model), vector autoregressive model VAR (Vecotr Atuo-regression Model) and state space model SSM (State Space Model) respectively for the time series of shipments, and respectively To perform predictive analysis:

[0046...

Embodiment 2

[0060] The optimal weighted combination forecasting method for manufacturing shipments as described in Example 1, the difference is that the specific method of the cointegration test in the step S2 is Engle-Granger cointegration test; the criterion for determining the lag period is : The optimal lag period is determined by the SIC and AIC information criteria.

Embodiment 3

[0062] The optimal weighted combined forecasting method for manufacturing shipments as described in Example 1, the difference is that the rule for establishing the optimal weighted combined forecasting model based on ARIMA-VAR-SSM in the step S3 is, e t has the smallest absolute value.

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Abstract

The invention relates to an optimum weighted composite prediction method for shipment amount of the manufacturing industry. The method comprises the following steps: respectively establishing an autoregressive integrated moving average (ARIMA) model, a vector autoregression (VAR) model and a state space model (SSM) for all the shipment amounts of the manufacturing industry; establishing an optimum weighted composite prediction model based on ARIMA-VAR-SSM; automatically solving related parameters of the optimum weighted composite predication model by utilizing an artificial bee colony algorithm; substituting a test sample into the ARIMA-VAR-SSM-based optimum weighted composite prediction model with parameters determined, so as to obtain a prediction result; and carrying out prediction error evaluation and analysis. The optimum weighted composite prediction method provided by the invention has the advantages that functions of inputting a time series and automatically acquiring the prediction result are realized, physical concept and thinking are clear, calculation is easy, dynamic characteristic of all the shipment amounts of the manufacturing industry can be directly predicted and reflected, prediction precision and accuracy are high, and practicability is strong.

Description

technical field [0001] The invention relates to an optimal weighted combination forecasting method for manufacturing shipments, and belongs to the technical field of big data applications. Background technique [0002] The manufacturing industry reflects the productivity level of a country and occupies an important share in the national economic development. As an important indicator of the manufacturing industry, shipments have an important impact on the development of the manufacturing industry, and deserve attention and research. Therefore, in order to achieve the healthy and sustainable development of the manufacturing industry and avoid the balance of supply and demand and the sharp fluctuations in prices, it is very important to strengthen the forecast of manufacturing shipments. [0003] At present, the methods used to forecast manufacturing shipments mainly include gray forecasting method, artificial neural network, integrated autoregressive moving average model (AR...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/04
CPCY02P90/30G06Q10/04G06Q50/04
Inventor 江铭炎陈蓓蓓郭宝峰
Owner SHANDONG UNIV
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