Enhancing KPI Forecasting Through Regression Algorithms Using Historical Data

Key performance indicators (KPIs) are essential tools for organizations across industries, providing a means to assess and enhance performance, efficiency, and quality. However, predicting KPIs presents challenges due to data nonlinearity, uncertainty, and variability. Traditional approaches, like trend analysis, may struggle to cope with these issues, leading to imperfect and unreliable predictions. In response, this research aims to enhance KPI prediction accuracy using machine learning algorithms. The study compares seven techniques—random forest (RF), XGBoost (XGB), decision tree (DT), linear regression (LR), support vector regression (SVR), neural network (NN), and multi-horizon quantile recurrent forecaster (MHQRF)—using three sales datasets. Evaluation includes four error measures: mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and R-squared. Results show MHQRF’s superior performance across all datasets, with the lowest MAE (618.6), MSE (1,505,991.394), and RMSE (683.98). However, XGB achieves a higher average R-squared value (0.886) compared to MHQRF, introducing more nuanced considerations for model selection in diverse KPI forecasting scenarios.