Precursors of extreme increments.
We investigate precursors and the predictability of extreme increments in a time series. The events we are focusing on consist in large increments within successive time steps. We are especially interested in understanding how the quality of the predictions depends on the strategy to choose precursors, on the size of the event, and on the correlation strength. We study the prediction of extreme increments analytically in an autoregressive process of order 1, and numerically in wind speed recordings and long-range correlated autoregressive moving average processes data. We evaluate the success of predictions via receiver-operator characteristics (ROC curves). Furthermore, we observe an increase of the quality of predictions with increasing event size and with decreasing correlation in all examples. Both effects can be understood by using the likelihood ratio as a summary index for smooth ROC curves.