Data mining meets network analysis: Traffic prediction models
| Autor: | Dzenana Donko, Teo Eterovic, Zeljko Juric, Sasa Mrdovic |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Computer science
business.industry Exponential smoothing Univariate Context (language use) Internet traffic Machine learning computer.software_genre Domain (software engineering) ComputingMethodologies_PATTERNRECOGNITION Artificial intelligence Autoregressive integrated moving average Data mining Time series business computer Network analysis |
| Zdroj: | MIPRO |
| DOI: | 10.1109/mipro.2014.6859800 |
| Popis: | Most research on network traffic prediction has been done on small datasets based on statistical methodologies. This research analyzes an internet traffic dataset spanning multiple months using the data mining process. Each data mining phase was carefully fitted to the network analysis domain and systematized in context of data mining. The second part of the paper evaluates various seasonal time series prediction models (univariate), including ANN, ARIMA, Holt Winters etc., as a data mining phase on the given dataset. The experiments have shown that in most cases ANNs are superior to other algorithms for this purpose. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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