Inflation Forecasting: LSTM Networks vs. Traditional Models for Accurate Predictions

Tormod Rygh, Camilla Vaage, Sjur Westgaard and Petter Eilif de Lange ()
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Tormod Rygh: Department of Economics, Norwegian University of Science and Technology, Klæbuveien 72, 7030 Trondheim, Norway
Camilla Vaage: Department of Economics, Norwegian University of Science and Technology, Klæbuveien 72, 7030 Trondheim, Norway
Sjur Westgaard: Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, Alfred Getz vei 3, 7041 Trondheim, Norway
Petter Eilif de Lange: Department of International Business, Norwegian University of Science and Technology, Larsgårdsvegen 2, 6065 Ålesund, Norway

JRFM, 2025, vol. 18, issue 7, 1-28

Abstract: This study investigates the effectiveness of neural network models, particularly LSTM networks, in enhancing the accuracy of inflation forecasting. We compare LSTM models with traditional univariate time series models such as SARIMA and AR(p) models, as well as machine learning approaches like LASSO regression. To improve the standard LSTM model, we apply advanced feature selection techniques and introduce data augmentation using the MBB method. Our analysis reveals that LASSO-LSTM hybrid models generally outperform LSTM models utilizing PCA for feature selection, particularly in datasets with multiple features, as measured by RMSE. However, despite these enhancements, LSTM models tend to underperform compared to simpler models like LASSO regression, AR(p), and SARIMA in the context of inflation forecasting. These findings suggest that, for policymakers and central bankers seeking reliable inflation forecasts, traditional models such as LASSO regression, AR(p), and SARIMA may offer more practical and accurate solutions.

Keywords: machine learning; LSTM; LASSO; inflation; forecasting (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2025
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