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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering
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A Novel Fuzzy Time Series Forecasting Model Based on Multiple Linear Regression and Time Series Clustering

机译：基于多线性回归和时间序列聚类的新型模糊时间序列预测模型

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摘要

Time series forecasting models based on a linear relationship model show great performance. However, these models cannot handle the the data that are incomplete, imprecise, and ambiguous as the interval-based fuzzy time series models since the process of fuzzification is abandoned. This article proposes a novel fuzzy time series forecasting model based on multiple linear regression and time series clustering for forecasting market prices. The proposed model employs a preprocessing to transform the set of fuzzy high-order time series into a set of high-order time series, with synthetic minority oversampling technique. After that, a high-order time series clustering algorithm based on the multiple linear regression model is proposed to cluster dataset of fuzzy time series and to build the linear regression model for each cluster. Then, we make forecasting by calculating the weighted sum of linear regression models’ results. Also, a learning algorithm is proposed to train the whole model, which applies artificial neural network to learn the weights of linear models. The interval-based fuzzification ensures the capability to deal with the uncertainties, and linear model and artificial neural network enable the proposed model to learn both of linear and nonlinear characteristics. The experiment results show that the proposed model improves the average forecasting accuracy rate and is more suitable for dealing with these uncertainties.
机译：基于线性关系模型的时间序列预测模型表现出色。然而，这些模型不能处理不完整，不精确和模糊的数据，因为基于间隔的模糊时间序列模型被抛弃过程。本文提出了一种基于多元线性回归和时间序列聚类的新型模糊时间序列预测模型，用于预测市场价格。该拟议的模型采用预处理来将模糊大奖时间序列的集合变为一组高阶时间序列，具有合成少数群体过采样技术。之后，提出了一种基于多元线性回归模型的大阶时间序列聚类算法，用于群集模糊时间序列的数据集，并为每个群集构建线性回归模型。然后，我们通过计算线性回归模型的加权和结果进行预测。此外，提出了一种学习算法来训练整个模型，其应用人工神经网络来学习线性模型的权重。基于间隔的模糊化确保了处理不确定性的能力，线性模型和人工神经网络使得可以学习线性和非线性特性的拟议模型。实验结果表明，该建议的模型提高了平均预测精度率，更适合处理这些不确定性。

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