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
  1. 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]
  2. 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]
  3. C. Nelson, H. Hapke, Building Machine Learning Pipelines by Hannes Hapke (O’Reilly Media, 2020) [Google Scholar]
  4. 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]
  5. Improve throughput, https://fts3-docs.web.cern.ch/fts3-docs/docs/ (2020), accessed: 15.12.2020 [Google Scholar]
  6. 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]
  7. 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]
  8. F. Karim, S. Majumdar, H. Darabi, S. Chen, LSTM Fully Convolutional Networks for Time Series Classification, IEEE Access 6, 1662 (2018) [Google Scholar]
  9. F.A. Gers, D. Eck, J. Schmidhuber, Applying LSTM to Time Series Predictable through Time-Window Approaches (2001), Springer, Berlin, Heidelberg [Google Scholar]
  10. 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) [Google Scholar]
  11. 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]
  12. 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]
  13. A. Al-Najjar, F. Pakzad, S. Layeghy, M. Portmann, Link Capacity Estimation in SDNbased End-hosts (2016) [Google Scholar]
  14. 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]
  15. Heteroscedasticity, https://www.statisticssolutions.com/heteroscedasticity/ (2021), accessed: 2021-02-25 [Google Scholar]
  16. L. Malyarets, K. Kovaleva, I. Lebedeva, I. Misiura, O. Dorokhov, The Heteroskedasticity Test Implementation for Linear Regression Model Using MATLAB, Informatica 42 (2018) [Google Scholar]
  17. 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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