This study investigates the performance of three time-domain features—Mean Absolute Value (MAV), Variance (VAR), and Root Mean Square (RMS)—in predicting angular motion using a time series dataset. The main problem addressed is the fluctuation in prediction accuracy over time, which is often overlooked in other studies that primarily focus on aggregate metrics. The aim of the research is to analyze and compare the prediction errors of these features, while also exploring their linearity with a normalization variable (‘nvar’) to enhance feature selection for predictive modeling. The method involves examining prediction errors over a 50-s period using normalized angles, and comparing the features using boxplots, scatter plots, and time series error analysis. Results show that MAV had a median error near zero with some outliers, while VAR exhibited larger negative errors, with a median around −2. RMS, on the other hand, displayed minimal variability, with errors concentrated around zero. Over the time series, prediction errors fluctuated between − 5 and 5 for all features, revealing dynamic changes in model accuracy. The strong linear relationships observed between the features and ‘nvar’ suggest that normalization improves feature performance. In conclusion, while MAV, VAR, and RMS are effective predictors, their errors vary over time, highlighting the need for temporal error analysis in predictive modeling. These findings emphasize the importance of feature selection and normalization to enhance model accuracy, which ranged from 0 to 1 in this study.