Abstract
As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, N) model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values (abbreviated as DBGM (1, N) model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models.
Highlights
Time series prediction has always been an important issue in economic, finance, marketing, as well as social problems
The advantages of the DBGM (1, N) model over the other grey models are demonstrated by four real cases. ese models include the GM (1, N) model, ARIMA, ANN, and the optimized GM (1, N) model based on the Simpson formula [24] (only optimization measures of the same type are meaningful for comparison; the authors only found an article discussing optimizing the background value of the GM (1, N) model; this article chooses to use the model proposed in this document for comparison)
We proposed a novel multivariate grey model based on dynamic background values (abbreviated as DBGM (1, N) model), along with a whale algorithmbased algorithm to optimize its unknown parameters. e DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy
Summary
Time series prediction has always been an important issue in economic, finance, marketing, as well as social problems. Hundreds of tools for analyzing time series have been developed, such as LR (linear regression), ARIMA (autoregressive integrated moving average) [1], and dendritic neuron model [2, 3]. These prediction models can only be established under the condition of large samples. Holing and sampling are an important means to analyze the oil and gas reserves of some region; the cost of holing is too high to drill many holes
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