An Empirical Study on Forecasting Using Decomposed Arrival Data of an Enterprise Computing System

Ngo, Linh; Apon, Amy; Hoffman, Doug

doi:10.1109/itng.2012.36

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Hamad et al investigated the near-term travel speed forecasting by using a Hilbert-Huang transform, which is actually a hybrid model of EMD and backpropagation neural network (1). Ngo et al used several forecasting techniques (vector autoregressive and ARIMA) in combination with EMD to investigate the trade-offs of EMD's decomposition (sifting) step for forecasting the arrival workload of an enterprise cluster (23). Results showed that EMD helps to improve forecasting results.…”

Section: Hybrid Forecasting Modelsmentioning

confidence: 99%

Empirical Mode Decomposition–Autoregressive Integrated Moving Average

Wang

Liu

Qian

et al. 2014

Transportation Research Record: Journal of the Transportation R

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Short-term freeway traffic speed prediction is essential to improving mobility and roadway safety. It has been a challenging and unresolved issue. Traffic speed prediction can be applied to enhance the intelligent freeway traffic management and control for applications such as operational and regulation planning. For example, with more reliable traffic speed prediction, the advanced traveler information system can provide travelers with predictive travel time information and optimal routing, which allows them to arrange their schedules accordingly. Moreover, traffic managers can use the predicted information to deploy various traffic management strategies to increase system efficiency. In this paper, a hybrid empirical mode decomposition (EMD) and autoregressive integrated moving average (ARIMA) (or EMD-ARIMA) approach was developed to predict the short-term traffic speed on freeways. In general, there were three stages in the hybrid EMD-ARIMA forecasting framework. The first was the EMD stage, which decomposed the freeway traffic speed time series data into a number of intrinsic mode function (IMF) components and a residue. The second stage was to find the appropriate ARIMA model for each IMF and residue and then make predictions on the basis of the appropriate ARIMA model. The third stage was to combine the prediction results of each IMF and residue to make the predictions. The experimental results indicated that the proposed hybrid EMD-ARIMA framework was capable of predicting short-term freeway traffic speed with high accuracy.

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Section: Hybrid Forecasting Modelsmentioning

confidence: 99%