Decision makers always have big challenge in analyze time series data. It is how to Find the best forecasting model. Time series models seasonal autoregressive integrated moving average (SARIMA), and Holt-Winters method) are methods that predict future values by analyzing past values. It plays an important role in many practical fields that depending on seasonal time series data.
This study is amid to build a good fit time series model of monthly Egyptian Consumer Price index (CPI) during the Ukraine -Russa war and forecasting the future values, using (CPI) data from Jan 2018 until June-2022. Applied on Autoregressive Integrated Moving Average (SARIMA) model and Holt-Winters method tool available in SPSS-26. The result reveal that the proposed models is shown to be adequate and with the original observations its forecasting is shown to agree closely. Also, according to the statical indicators, a Winters additive is better forecasting than ARIMA ((0,1,0) (0,0,1) Model) for the monthly Egyptian CPI data.