Real-time computation of empirical autocorrelation, and detection of non-stationary traffic conditions in high-speed networks

Stochastic traffic processes are open nonstationary (dynamic, transient), yet naive assumptions about stationarity lead to unrealistic forecasts. Format tests for stationarity are impractical in real time, and some heuristic tests are imprecise. Lagged autocorrelations are used in time series analysis for empirical stationarity tests, model identification and forecasting, but traditional methods are too slow for sub-second computation of empirical autocorrelation functions. We describe a mechanism for computing the empirical lag autocorrelation function of time series {X_i} in real time, and for using this function to detect nonstationarity conditions. Fuzzy logic is used to design a fast and accurate neural classifier of stationarity, The classifier´s estimate is updated with each new observation. No passes through sample datasets are necessary, and there is no need to overly compensate for round-off error. A real-time classifier of stationarity is fundamental to any sub-second traffic forecasting mechanism