Purpose

Conventional statistical forecasting methods typically need a large sample size or the use of overly confident hypotheses, like the Gaussian distribution of the input data. Unfortunately, these input data are frequently scarce or do no not follow a normal distribution law. A grey forecasting model can be developed and used to predict energy consumption for at least four data points or ambiguous data based on grey theory. The standard grey model, however, may occasionally result in significant forecasting errors.

Design/methodology/approach

In order to reduce these errors, this paper proposes a hybrid multivariate grey model (namely Grey Modeling (1,N)) optimized by Genetic Algorithms with sequential selection forecasting mechanism, abbreviated as Sequential-GMGA(1,N). A real case of Cameroon's oil products consumption is considered to demonstrate the effectiveness of the proposed forecasting model.

Findings

The results show the superiority of Sequential-GMGA(1,4) when compared with the results of competing grey models reported in the literature, with a mean absolute percentage error as low as 0.02%.

Originality/value

Without changing the model's basic structure, the suggested framework completely extracts the evolution law of multivariate time series. Regardless of data patterns, Sequential-GMGA(1,4) actively enhances all model parameters over the course of each predicted timeframe. Consequently, Sequential-GMGA(1,4) improves forecast accuracy by resolving the discrepancy between the model's least sum of squares of prediction errors and the parameterization approach based on grey derivative.

中文翻译：

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基于遗传算法优化的混合多元灰色模型及其在油品需求预测中的应用

目的

传统的统计预测方法通常需要大量样本或使用过于自信的假设，例如输入数据的高斯分布。不幸的是，这些输入数据通常很少或不遵循正态分布规律。基于灰色理论，可以开发灰色预测模型并用于预测至少四个数据点或模糊数据的能量消耗。然而，标准的灰色模型有时可能会导致重大的预测错误。

设计/方法/途径

为了减少这些误差，本文提出了一种基于序列选择预测机制的遗传算法优化的混合多元灰色模型（即Gray Modeling(1,N)），简称为Sequential-GMGA(1,N)。喀麦隆石油产品消费的真实案例被认为是证明所提出的预测模型的有效性。

发现

结果表明，与文献中报道的竞争灰色模型的结果相比，Sequential-GMGA(1,4) 具有优越性，平均绝对百分比误差低至 0.02%。

原创性/价值

在不改变模型基本结构的情况下，建议的框架完全提取了多元时间序列的演化规律。无论数据模式如何，Sequential-GMGA(1,4) 都会在每个预测时间范围内主动增强所有模型参数。因此，Sequential-GMGA(1,4)通过解决模型预测误差最小平方和与基于灰色导数的参数化方法之间的差异来提高预测精度。