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In this paper, Box-Jenkins’ Autoregressive Integrated Moving Average (ARIMA) model is implemented to forecast month over month inflation rates of Germany. Two alternative approaches are considered for forecasting—seasonal ARIMA and state space exponential smoothing model error, trend, seasonal (ETS). The two models are then compared for better results using time series cross-validation technique. Best ARIMA model was found to be (2, 0, 2) (1, 1, 1). Further, efforts were made to forecast, as accurate as possible, the future monthly inflation rates in Germany for a period of two years by fitting ARIMA model to our time series data. The forecast results have shown that the average inflation rate in 2023 is 3.9% and will further grow up to 5.9% in 2024. Based on MSE, the results confirm the accuracy of ARIMA model for forecasting inflation.

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Forecasting German Inflation Rates Using Seasonal ARIMA Model

  • 会议论文

  • 发表日期：

    2026年

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  • Kavita Gupta,
  • Manya Gupta

摘要

In this paper, Box-Jenkins’ Autoregressive Integrated Moving Average (ARIMA) model is implemented to forecast month over month inflation rates of Germany. Two alternative approaches are considered for forecasting—seasonal ARIMA and state space exponential smoothing model error, trend, seasonal (ETS). The two models are then compared for better results using time series cross-validation technique. Best ARIMA model was found to be (2, 0, 2) (1, 1, 1). Further, efforts were made to forecast, as accurate as possible, the future monthly inflation rates in Germany for a period of two years by fitting ARIMA model to our time series data. The forecast results have shown that the average inflation rate in 2023 is 3.9% and will further grow up to 5.9% in 2024. Based on MSE, the results confirm the accuracy of ARIMA model for forecasting inflation.