Inflation is a major determinant for allocation decisions and its forecast is a fundamental aim of governments and central banks. However, forecasting inflation is not a trivial task, as its prediction relies on low frequency, highly fluctuating data with unclear explanatory variables. While classical models show some possibility of predicting inflation, reliably beating the random walk benchmark remains difficult. Recently, (deep) neural networks have shown impressive results in a multitude of applications, increasingly setting the new state-of-the-art. This paper investigates the potential of the transformer deep neural network architecture to forecast different inflation rates. The results are compared to a study on classical time series and machine learning models. We show that our adapted transformer, on average, outperforms the baseline in 6 out of 16 experiments, showing best scores in two out of four investigated inflation rates. Our results demonstrate that a transformer based neural network can outperform classical regression and machine learning models in certain inflation rates and forecasting horizons.

翻译：通货膨胀是资产配置决策的主要决定因素，对其的预测是各国政府与中央银行的核心目标之一。然而，通胀预测并非易事，因为其依赖低频、高波动性且解释变量不明确的数据。尽管经典模型展现出一定的通胀预测可能性，但可靠地超越随机游走基准仍具挑战性。近年来，（深度）神经网络在众多应用中取得了令人瞩目的成果，并不断树立新的技术标杆。本文探究了Transformer深度神经网络架构在预测不同通胀率方面的潜力。将结果与经典时间序列及机器学习模型的研究进行了比较。研究表明，我们改进的Transformer在16组实验中平均在6组中优于基准模型，在研究的四种通胀率中两项取得最佳得分。实验结果证明，基于Transformer的神经网络在特定通胀率与预测期限中能够超越经典回归模型与机器学习模型。