The article introduces a new method for predicting future values in time series data, like energy use or global temperatures. By modifying the usual exponential moving average technique, the researchers developed a set of predictors that can outperform traditional methods like ARIMA. They show that their approach can be especially effective for time series with certain patterns of long-range dependence. The study demonstrates that these generalized exponential predictors offer a competitive alternative to existing forecasting techniques.