Nonlinear inflation forecasting with recurrent neural networks

Anna Almosova · Niek Andresen

67 citations0/0 files storedJournal of Forecasting

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Forecasting Techniques and Applications Neural Networks and Applications Stock Market Forecasting Methods

Abstract

Abstract Motivated by the recent literature that finds that artificial neural networks (NN) can efficiently predict economic time‐series in general and inflation in particular, we investigate if the forecasting performance can be improved even further by using a particular kind of NN—a recurrent neural network. We use a long short‐term memory recurrent neural network (LSTM) that was proven to be highly efficient for sequential data and computed univariate forecasts of monthly US CPI inflation. We show that even though LSTM slightly outperforms autoregressive model (AR), NN, and Markov‐switching models, its performance is on par with the seasonal autoregressive model SARIMA. Additionally, we conduct a sensitivity analysis with respect to hyperparameters and provide a qualitative interpretation of what the networks learn by applying a novel layer‐wise relevance propagation technique.

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References

2019DeepAR: Probabilistic forecasting with autoregressive recurrent networks 2007Why Has U.S. Inflation Become Harder to Forecast?2018Statistical and Machine Learning forecasting methods: Concerns and ways forward 1999Forecasting inflation 2005A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series 2010An Empirical Comparison of Machine Learning Models for Time Series Forecasting 2001Are Phillips Curves Useful for Forecasting Inflation?1995Forecasting exchange rates using feedforward and recurrent neural networks

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