Performative prediction is a phenomenon where a model’s predictions, or the decisions based on these predictions, may influence the outcomes of the model. This is especially conspicuous in a time series forecasting setting where interventions occur before outcomes are observed. These interventions dictate which data points in the time series can be used as inputs for future predictions. In this paper, we represent a patient’s symptom levels along their cancer rehabilitation plight as a time series. We use a decision-tree based model to predict the future symptom values of a patient. Based on these predictions, clinicians decide which symptom levels will be observed in the future. We propose methods to mitigate the problem of performative prediction in time series. Our results show how performative prediction may lead to a 29.4% to 40.7% higher error across different symptoms.