Hybridization of long short-term memory neural network in fractional time series modeling of inflation

被引:5
作者
Arif, Erman [1 ]
Herlinawati, Elin [2 ]
Devianto, Dodi [3 ]
Yollanda, Mutia [3 ]
Permana, Dony [4 ]
机构
[1] Univ Terbuka, Informat Syst Study Program, Tangerang Selatan, Indonesia
[2] Univ Terbuka, Math Study Program, Tangerang Selatan, Indonesia
[3] Univ Andalas, Dept Math & Data Sci, Padang, Indonesia
[4] Univ Negeri Padang, Dept Stat, Padang, Indonesia
来源
FRONTIERS IN BIG DATA | 2024年 / 6卷
关键词
inflation rate; ARFIMA; heteroscedasticity; ARFIMA-GARCH; ARFIMA-LSTM; LSTM; MARKET;
D O I
10.3389/fdata.2023.1282541
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
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页数:16
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