which is a better explaination: To make forecasting models more robust and accurate, here are a few strategies we can consider: Choosing the right model is important when dealing with non-stationary data. Models like SARIMA (Seasonal ARIMA) are capable of handling non-stationary data and incorporating seasonality, which improves forecast accuracy. To ensure the reliability and generalizability of the model, cross-validation techniques should be used. These techniques assess the performance of the model on unseen data, guarding against overfitting. On the other hand, non-stationarity can be managed through preprocessing methods like differencing and transformations. Stationarity tests, such as the Augmented Dickey-Fuller (ADF) test and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, can be conducted to assess the stationarity of the data. The ADF test helps identify unit roots, which are indicative of non-stationarity, while the KPSS test detects trend stationarity. These tests provide valuable insights into the appropriate data transformations needed to achieve stationarity. Sometimes, using multiple models and averaging their predictions can lead to better results. This method, known as ensemble modeling, helps balance out the strengths and weaknesses of individual models. Comparing the forecasting model against a simple random walk model helps in assessing its added value. If the model outperforms the random walk, it indicates that it captures more information from the