Inflation forecasting has been and continues to be an important issue for the world’s economies. Governments, through their central banks, watch closely inflation indicators to make national decisions and policies. This study proposes to forecast the inflation rate in five Latin American emerging economies based on the commonly used seasonal autoregressive integrated moving average (SARIMA) approach combined with long short-term memory (LSTM). Additionally, we run forecasts based on fuzzy inference systems (FISs), artificial neural networks (ANNs), artificial neuro-FIS, and SARIMA ANN as benchmarks to compare the performance of the combines SARIMA–LSTM. The combined SARIMA–LSTM captures the linear aspects of the time series as well as the nonlinear aspects. The results indicate that the proposed model based on the combination of SARIMA and LSTM has higher accuracy in inflation forecasts over the SARIMA and LSTM separately.

通货膨胀预测一直并将继续成为世界经济的一个重要问题。各国政府通过其中央银行密切关注通胀指标，以制定国家决策和政策。本研究建议基于常用的季节性自回归综合移动平均 (SARIMA) 方法结合长短期记忆 (LSTM) 来预测五个拉丁美洲新兴经济体的通货膨胀率。此外，我们运行基于模糊推理系统 (FIS)、人工神经网络 (ANN)、人工神经 FIS 和 SARIMA ANN 作为基准的预测，以比较组合 SARIMA-LSTM 的性能。组合的 SARIMA-LSTM 捕获时间序列的线性方面以及非线性方面。