Guaranteed Network Traffic Demand Prediction Using FARIMA Models
INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2008(2008)
摘要
The Fractional Auto-Regressive Integrated Moving Average (FARIMA) model is often used to model and predict network traffic demand which exhibits both long-range and short-range dependence. However, finding the best model to fit a given set of observations and achieving good performance is still an open problem. We present a strategy, namely Aggregating Algorithm, which uses several FARIMA models and then aggregates their outputs to achieve a guaranteed (in a sense) performance. Our feasibility study experiments on the public datasets demonstrate that using the Aggregating Algorithm with FARIMA models is a useful tool in predicting network traffic demand.
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关键词
feasibility study experiment,best model,open problem,short-range dependence,public datasets,aggregating algorithm,good performance,guaranteed network traffic demand,farima models,network traffic demand,fractional auto-regressive integrated,farima model,feasibility study
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