在通货膨胀的分数时间序列建模中混合使用长短期记忆神经网络

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Erman Arif, Elin Herlinawati, D. Devianto, Mutia Yollanda, Dony Permana
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引用次数: 0

摘要

通货膨胀能够对货币政策产生重大影响,因此强调需要准确的预测来指导旨在稳定通货膨胀率的决策。鉴于通货膨胀与货币之间的重要关系,在数据中检测长期记忆模式变得可行。为了捕捉这些长记忆模式,自回归分位移平均法(ARFIMA)作为数据挖掘的重要工具应运而生。由于残差假设带来的挑战,必须开发时间序列模型来解决异方差问题。因此,必须实施一个合适的模型来纠正残差 ARFIMA 中的这种效应。在这种情况下,提出了一种新的混合模型,用长短期记忆(LSTM)神经网络取代广义自回归条件异方差(GARCH)。该网络被用作迭代模型来解决这一问题,并获得最佳参数。通过使用平均绝对百分比误差 (MAPE)、平均平方误差 (MSE) 和平均绝对误差 (MAE) 进行敏感性分析,评估了 ARFIMA、ARFIMA-GARCH 和 ARFIMA-LSTM 模型的性能。结果表明,ARFIMA-LSTM 在模拟通货膨胀率方面表现出色。这进一步证明了通货膨胀数据具有长记忆的特点,而通过整合 LSTM 神经网络,模型的准确性得到了提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybridization of long short-term memory neural network in fractional time series modeling of inflation
Inflation is capable of significantly impacting monetary policy, thereby emphasizing the need for accurate forecasts to guide decisions aimed at stabilizing inflation rates. Given the significant relationship between inflation and monetary, it becomes feasible to detect long-memory patterns within the data. To capture these long-memory patterns, Autoregressive Fractionally Moving Average (ARFIMA) was developed as a valuable tool in data mining. Due to the challenges posed in residual assumptions, time series model has to be developed to address heteroscedasticity. Consequently, the implementation of a suitable model was imperative to rectify this effect within the residual ARFIMA. In this context, a novel hybrid model was proposed, with Generalized Autoregressive Conditional Heteroscedasticity (GARCH) being replaced by Long Short-Term Memory (LSTM) neural network. The network was used as iterative model to address this issue and achieve optimal parameters. Through a sensitivity analysis using mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), the performance of ARFIMA, ARFIMA-GARCH, and ARFIMA-LSTM models was assessed. The results showed that ARFIMA-LSTM excelled in simulating the inflation rate. This provided further evidence that inflation data showed characteristics of long memory, and the accuracy of the model was improved by integrating LSTM neural network.
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来源期刊
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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