Polymorphic SBD Preprocessor: A Preprocessing Approach for Social Big Data

Jadiya, Amit K.; Chaudhary, Archana; Thakur, Ramesh

doi:10.21817/indjcse/2020/v11i6/201106169

Web-based E-Commerce Customer Segmentation System Using RFM and K-Means Model

Hilmy,

Nurhaliza,

Huzyan Octava

et al. 2023

2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

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Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

Gur

2024

DSFE

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<p>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.</p>

show abstract

Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

Gur

2024

DSFE

0

View full text Add to dashboard Cite

<p>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.</p>

show abstract

Polymorphic SBD Preprocessor: A Preprocessing Approach for Social Big Data

Cited by 3 publications

References 13 publications

Web-based E-Commerce Customer Segmentation System Using RFM and K-Means Model

Web-based E-Commerce Customer Segmentation System Using RFM and K-Means Model

Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

Development and application of machine learning models in US consumer price index forecasting: Analysis of a hybrid approach

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