Open Access
| Issue |
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|
|
|---|---|---|
| Article Number | 02050 | |
| Number of page(s) | 11 | |
| Section | Distributed Computing, Data Management and Facilities | |
| DOI | https://doi.org/10.1051/epjconf/202125102050 | |
| Published online | 23 August 2021 | |
- E. Karavakis, A. Manzi, M.A. Rios, O. Keeble, C.G. Cabot, M. Simon, M. Patrascoiu, A. Angelogiannopoulos, FTS improvements for LHC Run-3 and beyond, EPJ Web of Conferences 245 (2020) [Google Scholar]
- J. Waczynska, E. Martelli, E. Karavakis, T. Cass, NOTED: a framework to optimize the network traffc via theanalysis of data set from transfers services as FTS., Paper presented to vCHEP 2021s (2021) [Google Scholar]
- C. Nelson, H. Hapke, Building Machine Learning Pipelines by Hannes Hapke (O’Reilly Media, 2020) [Google Scholar]
- A. Lazaris, V.K. Prasanna, An LSTM Framework For Modeling Network Traffc, in 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) (2019), pp. 19–24 [Google Scholar]
- Improve throughput, https://fts3-docs.web.cern.ch/fts3-docs/docs/ (2020), accessed: 15.12.2020 [Google Scholar]
- T. Zhang, S. Song, S. Li, L. Ma, S. Pan, L. Han, Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series., Energies 12 (2019) [Google Scholar]
- X. Shi, Z. Chen, H. Wang, D.Y. Yeung, W.k. Wong, W.C. Woo, Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting, NIPS’ 15 p. 802–810 (2015) [Google Scholar]
- F. Karim, S. Majumdar, H. Darabi, S. Chen, LSTM Fully Convolutional Networks for Time Series Classification, IEEE Access 6, 1662 (2018) [Google Scholar]
- F.A. Gers, D. Eck, J. Schmidhuber, Applying LSTM to Time Series Predictable through Time-Window Approaches (2001), Springer, Berlin, Heidelberg [Google Scholar]
- X. Song, F. Yang, D. Wang, K. Tsui, Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries, IEEE Access 7, 88894 (2019) [CrossRef] [Google Scholar]
- I.E. Livieris, E. Pintelas, P. Pintelas, A CNN–LSTM model for gold price time-series forecasting, Neural computing and applications 32, 17351 (2020) [Google Scholar]
- H. Zheng, F. Lin, X. Feng, Y. Chen, A hybrid deep learning model with attentionbased conv-LSTM networks for short-term traffc flow prediction, IEEE Transactions on Intelligent Transportation Systems (2020) [Google Scholar]
- A. Al-Najjar, F. Pakzad, S. Layeghy, M. Portmann, Link Capacity Estimation in SDNbased End-hosts (2016) [Google Scholar]
- J. Hauke, T. Kossowski, Comparison of values of Pearson’s and Spearman’s correlation coeffcients on the same sets of data, Quaestiones geographicae 30, 87 (2011) [Google Scholar]
- Heteroscedasticity, https://www.statisticssolutions.com/heteroscedasticity/ (2021), accessed: 2021-02-25 [Google Scholar]
- L. Malyarets, K. Kovaleva, I. Lebedeva, I. Misiura, O. Dorokhov, The Heteroskedasticity Test Implementation for Linear Regression Model Using MATLAB, Informatica 42 (2018) [CrossRef] [Google Scholar]
- Y. Yin, R. Carroll, A diagnostic for heteroscedasticity based on the spearman rank correlation, Statistics & Probability Letters 10, 69 (1990) [Google Scholar]
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