Remaining time prediction (RTP) is the problem of predicting the time until a specific process step is reached in a specific process instance. Feature engineering in established RTP techniques assume that cases progress in isolation. Intercase dynamics such as batching violate this assumption, leading to high prediction errors. Yet, existing RTP techniques do not consider the nature of prediction errors to improve quality. We contribute a technique for identifying the location and context of prediction errors by visually comparing prediction and ground truth. For the case of batching, we show how to engineer inter-case features that detail the impact of batching on the remaining time. Our evaluation shows that adding intercase features improves prediction performance across almost all evaluated primary prediction methods on two real-life event logs, with error reductions of up to 37%. We finally advocate for a more thorough and transparent evaluation of prediction errors in RTP research, including our own results.