Across the sciences, time moves in rhythms — disease, population, price, and behavior all echo their own pasts in ways that careful observation can reveal. A research team has now formalized that intuition, developing a method that detects recurring temporal patterns within sequential data and feeds them as dynamic covariates into forecasting models. Tested across one thousand time series spanning epidemiology, ecology, and social science, the approach consistently improved prediction accuracy in ARIMA, Random Forest, and LSTM models alike. It is a reminder that the past does not merely preced