Objective: Seizure risk is modulated by multiscale brain rhythms. Previous studies using cycles in electroencephalography, heart rate, and wearable data suggest the possibility of forecasting seizures days in advance. However, they commonly rely on methods requiring (days of) information from time points beyond the moment of forecast (noncausal processing). Although applicable to retrospective analyses, such an approach is not feasible for real-time clinical applications. Here, we systematically investigated the impact of using only past data (causal processing) to estimate long-timescale cycles of epileptic brain activity on forecasting performance. Methods: We analyzed long-term interictal epileptiform activity (IEA) recordings from 18 patients implanted with the RNS system. Circadian/multidien IEA cycles were extracted using two approaches: (1) noncausal filters also requiring future data; and (2) causal filters utilizing only past data, simulating real-time scenarios. Forecasting was performed using Poisson regression, and the performance was benchmarked against established baseline models. Results: Noncausal models achieved high performance, consistent with previous literature. However, when strict causal constraints were applied, performance deteriorated significantly ((Formula presented.), Wilcoxon signed-rank test). For hourly forecasting, the median area under the curve dropped from.76 to.63, time in warning increased from 35% to 60%, and patients with above-chance performance decreased from 89% to 72%. Crucially, for daily forecasting, the causal models failed to perform better than chance for all patients. Performance loss was attributable to the input–output time lag and cycle deformation inherent to filters when analyzing data in a real-time scenario without access to future signal trends. Significance: Clinically beneficial seizure forecasting requires algorithms that function prospectively, using only past data. Our findings reveal a substantial performance gap between theoretical (noncausal, retrospective) performance and realistic (causal, prospective) capabilities. This suggests that prior studies may have overestimated forecasting accuracy. Future research must focus on developing novel cycle-extraction methods that remain robust under real-time prospective conditions.