Prediction of Network Throughput using ARIMA
- Title
- Prediction of Network Throughput using ARIMA
- Author
- 이주현
- Keywords
- ARIMA; Time series data; Network Throughput; Prediction
- Issue Date
- 2020-02
- Publisher
- The Korean Institute of Communications and Information Sciences (KICS)
- Citation
- 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), page. 1-5
- Abstract
- In this paper, we apply an ARIMA (Autoregressive Integrated Moving Average) model to predict future network throughput, which is important in improving the network protocols in terms of latency, energy, etc. The Autoregressive Integrated Moving Average (ARIMA) model is a popular and a successful method to predict time-series data. It has wide applications in time-series analysis, including statistics and economics. We first train the model with the network throughput data using history. Then we make an estimation for the throughput in the future. We use the Mean Squared Error (MSE) as a means of
error estimation and tune the parameters p, d, q, m of the ARIMA model. As a result, we obtain a forecast waveform with an average error rate of 2.84%.
- URI
- https://ieeexplore.ieee.org/document/9065083?arnumber=9065083&SID=EBSCO:edseeehttps://repository.hanyang.ac.kr/handle/20.500.11754/163169
- ISBN
- 978-1-7281-4985-1
- DOI
- 10.1109/ICAIIC48513.2020.9065083
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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