In the context of Industrial Edge Computing, the growing deployment of IoT and mobile devices has resulted in an explosion of real-time, high-velocity time series data. This paper investigates statistical and machine learning approaches for time series forecasting in such environments, where latency, bandwidth, and computational efficiency are critical constraints. We evaluate traditional methods like Simple Moving Average (SMA), Holt-Winters Exponential Smoothing, and ARIMA, and contrast them with machine learning models such as Logistic Regression and XGBoost. Experiments conducted on the Microsoft Azure Predictive Maintenance dataset demonstrate that SMA and ARIMA offer comparable baseline accuracy, while XGBoost outperforms them in terms of forecast quality for multivariate series. We also explore the effectiveness of SMOTE for improving failure prediction using logistic regression. The findings suggest that lightweight models like XGBoost with lag feature engineering can be viable for forecasting in edge environments.