Machine learning approaches for forecasting inflation: empirical evidence from Sri Lanka
The aim of this study is to forecast the inflation rate using supervised machine learning models (SMLM). While SMLMs are widely used in various fields, they have also been widely applied to forecast inflation rates. Therefore, the main objective of this study is to identify the best model for foreca...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Ruhuna
2023-06-01
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| Series: | Ruhuna Journal of Science |
| Subjects: | |
| Online Access: | https://www.rjs.ruh.ac.lk/index.php/rjs/article/view/5575/375 |
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| Summary: | The aim of this study is to forecast the inflation rate using supervised machine learning models (SMLM). While SMLMs are widely used in various fields, they have also been widely applied to forecast inflation rates. Therefore, the main objective of this study is to identify the best model for forecasting inflation among four different SMLMs: LASSO regression (LR), Bayesian Ridge Regression (BRR), Support Vector Machine Regression (SVR), and Random Forest Regression (RFR) models. To achieve this objective, two different types of cross-validation techniques were used: K-fold cross-validation method (KCV) and walk forward validation (WFV) methods. These techniques were used to estimate the parameters and hyper-parameters for each machine learning model with the aid of root mean square error. The mean absolute percentage error (MAPE) was used to compare the performance of the different SMLMs. Empirical evidence from Sri Lanka between 1988 and 2021 was used to test the performance of the SMLMs in forecasting inflation rates. The results show that the LR model with walk forward validation is the best method for forecasting the future inflation rate of Sri Lanka based on the MAPE value. Overall, this study demonstrates the effectiveness of SMLMs in forecasting inflation rates under the critical conditions and highlights the importance of employing appropriate cross-validation techniques when using these SMLM models. The findings of this study can provide valuable insights for policymakers, investors, and researchers who are interested in forecasting inflation rates.
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| ISSN: | 2536-8400 |