Detailed Information on Publication Record

@inproceedings{34693,
   author = {Jirsík, Tomáš and Trčka, Štěpán and Čeleda, Pavel},
   address = {Washington DC, USA},
   booktitle = {2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)},
   keywords = {quality of service; forecast; long short-term memory; neural network},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Washington DC, USA},
   isbn = {978-1-72810-618-2},
   pages = {251-260},
   publisher = {IEEE},
   title = {Quality of Service Forecasting with LSTM Neural Network},
   url = {http://dl.ifip.org/db/conf/im/im2019/188793.pdf},
   year = {2019}
}

TY  - JOUR
ID  - 34693
AU  - Jirsík, Tomáš - Trčka, Štěpán - Čeleda, Pavel
PY  - 2019
TI  - Quality of Service Forecasting with LSTM Neural Network
PB  - IEEE
CY  - Washington DC, USA
SN  - 9781728106182
KW  - quality of service
KW  - forecast
KW  - long short-term memory
KW  - neural network
UR  - http://dl.ifip.org/db/conf/im/im2019/188793.pdf
N2  - A robust and accurate forecast of the Quality of Service (QoS) attributes is essential for effective web service recommendation, enhanced user experience, and service management. Deep learning methods, especially Long Short-Term Memory Neural Networks (LSTM NN), have proven to be worthy for sequence forecasting in various domains recently. In this paper, we pilot an experimental application of LSTM NN in the domain of QoS forecasting. We develop a LSTM NN model for QoS prediction and compare its forecast performance with existing approaches for QoS attribute forecasting -- ARIMA and Holt-Winters models. The approaches are compared on two real-world QoS attribute datasets created using centralized passive QoS attribute collection technique. Our results show that LSTM NN improves the accuracy of QoS forecast for attributes collected with high granularity while maintaining a reasonable computation time.
ER  - 

JIRSÍK, Tomáš, Štěpán TRČKA and Pavel ČELEDA. Quality of Service Forecasting with LSTM Neural Network. Online. In \textit{2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)}. Washington DC, USA: IEEE, 2019, p.~251-260. ISBN~978-1-72810-618-2.