A Forecasting Capability Study of Empirical Mode Decomposition for the Arrival Time of a Parallel Batch System

This paper demonstrates the feasibility and potential of applying empirical mode decomposition (EMD) to forecast the arrival time behaviors in a parallel batch system. An analysis of the workload records shows the existence of daily and weekly patterns within the workload. Results show that the intrinsic mode functions (IMF), products of the sifting/decomposition process of EMD, produce a better prediction than the original arrival histogram when used in a simple weight-matching prediction technique. Promising applications include the implementation of an EMD/neural network combination. Key Words: Empirical Mode Decomposition, forecasting, time series, neural network, workload.

• Type: Conference
• Year: 2010
• Where: ITNG
• Authors: Linh Ngo, Amy W. Apon, Doug Hoffman
• Keywords: Arrival Time Behaviors, Empirical Mode Decomposition, Intrinsic Mode Functions, ITNG 2010