📄 Short Report: Model Generalization Discussion Both ARIMA and LSTM models were evaluated using a rolling window approach over 40 windows. The results indicate that LSTM slightly outperforms ARIMA, with an average RMSE of 3,169,389 compared to ARIMA’s 3,175,011. While the absolute difference is small (≈0.2%), the consistency of LSTM predictions is notable, as reflected in its lower RMSE standard deviation (729,371 vs. 763,036). The reason LSTM generalizes better is that it can capture complex nonlinear temporal dependencies in the sales data, which ARIMA (a linear statistical model) cannot fully represent. This advantage is especially relevant for retail sales, where seasonality, promotions, and external factors often introduce nonlinear fluctuations. In terms of stability, LSTM’s tighter error range suggests that it adapts more consistently across different rolling windows, further supporting its robustness. While ARIMA remains a strong baseline for time series forecasting, LSTM demonstrates better generalization capability due to its ability to learn hidden patterns that extend beyond simple trend and seasonality. Conclusion: The LSTM model generalizes better than ARIMA for this dataset because it handles complex patterns and provides more stable performance, making it the preferred choice for future forecasting.
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