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

This study investigates the effectiveness of neural network models, particularly Long Short-Term Memory (LSTM) networks, in enhancing the accuracy of inflation forecasting. We compare LSTM models with traditional univariate time series models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) and Autoregressive (AR(p)) models, as well as machine learning approaches like Least Absolute Shrinkage and Selection Operator (LASSO) regression. To improve the standard LSTM model, we apply advanced feature selection techniques and introduce data augmentation using the Moving Block Bootstrapping (MBB) method. Our analysis reveals that LASSO-LSTM hybrid models generally outperform LSTM models utilizing Principal Component Analysis (PCA) for feature selection, particularly in datasets with multiple features, as measured by Root Mean Square Error (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.