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 t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-01-01
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| Series: | Frontiers in Big Data |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2023.1282541/full |
| _version_ | 1827391102889492480 |
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| author | Erman Arif Elin Herlinawati Dodi Devianto Mutia Yollanda Dony Permana |
| author_facet | Erman Arif Elin Herlinawati Dodi Devianto Mutia Yollanda Dony Permana |
| author_sort | Erman Arif |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T17:05:51Z |
| format | Article |
| id | doaj.art-57e1701a66d94cd8809724ca49758fde |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-03-08T17:05:51Z |
| publishDate | 2024-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-57e1701a66d94cd8809724ca49758fde2024-01-04T05:01:24ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2024-01-01610.3389/fdata.2023.12825411282541Hybridization of long short-term memory neural network in fractional time series modeling of inflationErman Arif0Elin Herlinawati1Dodi Devianto2Mutia Yollanda3Dony Permana4Information system study program, Universitas Terbuka, Tangerang Selatan, IndonesiaMathematics study program, Universitas Terbuka, Tangerang Selatan, IndonesiaDepartment of Mathematics and Data Science, Universitas Andalas, Padang, IndonesiaDepartment of Mathematics and Data Science, Universitas Andalas, Padang, IndonesiaDepartment of Statistics, Universitas Negeri Padang, Padang, IndonesiaInflation 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.https://www.frontiersin.org/articles/10.3389/fdata.2023.1282541/fullinflation rateARFIMAheteroscedasticityARFIMA-GARCHARFIMA-LSTM |
| spellingShingle | Erman Arif Elin Herlinawati Dodi Devianto Mutia Yollanda Dony Permana Hybridization of long short-term memory neural network in fractional time series modeling of inflation Frontiers in Big Data inflation rate ARFIMA heteroscedasticity ARFIMA-GARCH ARFIMA-LSTM |
| title | Hybridization of long short-term memory neural network in fractional time series modeling of inflation |
| title_full | Hybridization of long short-term memory neural network in fractional time series modeling of inflation |
| title_fullStr | Hybridization of long short-term memory neural network in fractional time series modeling of inflation |
| title_full_unstemmed | Hybridization of long short-term memory neural network in fractional time series modeling of inflation |
| title_short | Hybridization of long short-term memory neural network in fractional time series modeling of inflation |
| title_sort | hybridization of long short term memory neural network in fractional time series modeling of inflation |
| topic | inflation rate ARFIMA heteroscedasticity ARFIMA-GARCH ARFIMA-LSTM |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2023.1282541/full |
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