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http://repository.unmul.ac.id/handle/123456789/3253Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Purnawansyah, Purnawansyah | - |
| dc.contributor.author | Haviluddin, Haviluddin | - |
| dc.contributor.author | Alfred, Rayner | - |
| dc.contributor.author | Gaffar, Achmad Fanany Onnlita | - |
| dc.date.accessioned | 2019-12-13T00:26:00Z | - |
| dc.date.available | 2019-12-13T00:26:00Z | - |
| dc.date.issued | 2018-01-02 | - |
| dc.identifier.issn | 2597-4602 | - |
| dc.identifier.uri | http://repository.unmul.ac.id/handle/123456789/3253 | - |
| dc.description.abstract | This paper presents an approach for a network traffic characterization by using statistical techniques. These techniques are obtained using the decomposition, winter’s exponential smoothing and autoregressive integrated moving average (ARIMA). In this paper, decomposition and winter’s exponential smoothing techniques were used additive and multiplicative model. Then, ARIMA based-on Box-Jenkins methodology. The results of ARIMA (1,0,2) was shown the best model that can be used to the internet network traffic forecasting. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Knowledge Engineering and Data Science (KEDS) | en_US |
| dc.subject | Decomposition, Winter’s exponential smoothing, ARIMA, Additive, Multiplicative | en_US |
| dc.title | Network Traffic Time Series Performance Analysis using Statistical Methods | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | J - Computer Sciences and Information Technology | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 9. KEDS2018_Network traffic prediction.pdf | 591.66 kB | Adobe PDF | View/Open |
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