Forecasting is of prime importance for accuracy in decision making. For data sets containing high autocorrelations, failure to account for temporal dependence will result in poor ...
Motivated by a broad range of potential applications, we address the quantile prediction problem of real-valued time series. We present a sequential quantile forecasting model bas...
Artificial Neural Networks are universal and highly flexible function approximators first used in the fields of cognitive science and engineering. In recent years, Neural Networks...
Effort to evolve and maintain a software system is likely to vary depending on the amount and frequency of change requests. This paper proposes to model change requests as time se...
Abstract-- In this paper, we address the issue of forecasting for periodically measured nonstationary traffic based on statistical time series modeling. Often with time series base...