Accurate predictions are crucial for informed decisions, even when only historical data is available and traditional time series models like ARIMA are not applicable. This work aims to develop a generic forecasting model that delivers robust predictions through feature engineering and prediction algorithms. Enhanced by time series data, diagnostic and autocorrelation analysis, the model's explainability is improved. The goal is to ensure data and model quality, promote operational excellence and…