用于通货膨胀预测的混合顺序系统

André L. S. Xavier, Bruno José Torres Fernandes, J. F. D. de Oliveira
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引用次数: 0

摘要

经济价格指数预测因其对各个部门和市场的重大影响而受到学者和决策者的关注。如果不通过模型和宏观经济研究对通货膨胀进行适当的控制,经济的稳定就会受到威胁,因此,预测通货膨胀是政府和企业制定政策的重要任务。在这个意义上,本研究工作试图以巴西、美利坚合众国和日本的通货膨胀率为模型。在时间序列领域,文献表明将线性和非线性模型相结合来模拟通货膨胀。本文提出了一种混合ARIMA-MLP系统来映射线性和非线性模式。这是利用一个混合进化系统,包括一个简单的指数滤波器,线性ARIMA和自回归(AR)模型和一个多层感知器模型。此外,它还实现了指数平滑模型(ETS),分次回归(QR)和支持向量机。实验结果表明,该混合进化系统在预测领域取得了令人满意的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A hybrid sequential system for inflation forecasting
Forecasting price indexes of the economy has received an attention from scholars and policy makers due to its significant effect on various sectors and markets. The stability of economy is at risk if inflation is not properly checked through models and macroeconomic studies, therefore, forecasting inflation is an important task for the formulation of policies in governments and companies. In this sense, this research work attempts to model inflation rate to Brazil, United State of America and Japan. The literature, in the field of time series, indicates the combination of linear and non-linear models to model inflation. In this paper, a hybrid ARIMA-MLP system has been proposed to map linear and nonlinear patterns. This is explored using a hybrid evolutionary system consisting of a simple exponential filter, linear ARIMA and autoregressive (AR) models and a Multilayer Perceptron model. In addition, it was also implemented exponential smoothing models (ETS), Qunatile Regression (QR) and Support Vector Machines. The experimental results show that the hybrid evolutionary system presented promising results in the prediction domain.
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