Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach
This study aims to apply advanced machine-learning models and hybrid approaches to improve the forecasting accuracy of the US Consumer Price Index (CPI). The study examined the performance of LSTM, MARS, XGBoost, LSTM-MARS, and LSTM-XGBoost models using a large time-series data from January 1974 to...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-10-01
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| Series: | Data Science in Finance and Economics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/DSFE.2024020 |
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| author | Yunus Emre Gur |
| author_facet | Yunus Emre Gur |
| author_sort | Yunus Emre Gur |
| collection | DOAJ |
| description | This study aims to apply advanced machine-learning models and hybrid approaches to improve the forecasting accuracy of the US Consumer Price Index (CPI). The study examined the performance of LSTM, MARS, XGBoost, LSTM-MARS, and LSTM-XGBoost models using a large time-series data from January 1974 to October 2023. The data were combined with key economic indicators of the US, and the hyperparameters of the forecasting models were optimized using genetic algorithm and Bayesian optimization methods. According to the VAR model results, variables such as past values of CPI, oil prices (OP), and gross domestic product (GDP) have strong and significant effects on CPI. In particular, the LSTM-XGBoost model provided superior accuracy in CPI forecasts compared with other models and was found to perform the best by establishing strong relationships with variables such as the federal funds rate (FFER) and GDP. These results suggest that hybrid approaches can significantly improve economic forecasts and provide valuable insights for policymakers, investors, and market analysts. |
| format | Article |
| id | doaj-art-d6fd9ff48f8d4fe4b2db8be9cd8b9d92 |
| institution | Kabale University |
| issn | 2769-2140 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Data Science in Finance and Economics |
| spelling | doaj-art-d6fd9ff48f8d4fe4b2db8be9cd8b9d922025-01-24T01:03:03ZengAIMS PressData Science in Finance and Economics2769-21402024-10-014446951310.3934/DSFE.2024020Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approachYunus Emre Gur0Department of Management Information Systems, Fırat University, Elazig, Centre, 23100, TurkeyThis study aims to apply advanced machine-learning models and hybrid approaches to improve the forecasting accuracy of the US Consumer Price Index (CPI). The study examined the performance of LSTM, MARS, XGBoost, LSTM-MARS, and LSTM-XGBoost models using a large time-series data from January 1974 to October 2023. The data were combined with key economic indicators of the US, and the hyperparameters of the forecasting models were optimized using genetic algorithm and Bayesian optimization methods. According to the VAR model results, variables such as past values of CPI, oil prices (OP), and gross domestic product (GDP) have strong and significant effects on CPI. In particular, the LSTM-XGBoost model provided superior accuracy in CPI forecasts compared with other models and was found to perform the best by establishing strong relationships with variables such as the federal funds rate (FFER) and GDP. These results suggest that hybrid approaches can significantly improve economic forecasts and provide valuable insights for policymakers, investors, and market analysts.https://www.aimspress.com/article/doi/10.3934/DSFE.2024020consumer price index (cpi)hyperparameter optimizationhybrid modelsmachine learningmacroeconomic indicators |
| spellingShingle | Yunus Emre Gur Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach Data Science in Finance and Economics consumer price index (cpi) hyperparameter optimization hybrid models machine learning macroeconomic indicators |
| title | Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach |
| title_full | Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach |
| title_fullStr | Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach |
| title_full_unstemmed | Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach |
| title_short | Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach |
| title_sort | development and application of machine learning models in us consumer price index forecasting analysis of a hybrid approach |
| topic | consumer price index (cpi) hyperparameter optimization hybrid models machine learning macroeconomic indicators |
| url | https://www.aimspress.com/article/doi/10.3934/DSFE.2024020 |
| work_keys_str_mv | AT yunusemregur developmentandapplicationofmachinelearningmodelsinusconsumerpriceindexforecastinganalysisofahybridapproach |