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A Forecasting Capability Study of Empirical Mode Decomposition for the Arrival Time of a Parallel Batch System

Linh Ngo, Amy Apon, Doug Hoffman
2010 2010 Seventh International Conference on Information Technology: New Generations  
This paper demonstrates the feasibility and potential of applying empirical mode decomposition (EMD) to forecast the arrival time behaviors in a parallel batch system.  ...  Results show that the intrinsic mode functions (IMF), products of the sifting/decomposition process of EMD, produce a better prediction than the original arrival histogram when used in a simple weight-matching  ...  Introduction Previous work [1] demonstrated the ability of Empirical Mode Decomposition [2] for the characterization of workloads.  ... 
doi:10.1109/itng.2010.138 dblp:conf/itng/NgoAH10 fatcat:yhhdfnebpvhdzb3c4rte547ljy

An Empirical Study on Forecasting Using Decomposed Arrival Data of an Enterprise Computing System

Linh Bao Ngo, Amy Apon, Doug Hoffman
2012 2012 Ninth International Conference on Information Technology - New Generations  
The research is based on earlier work on the forecasting potential of empirical mode decomposition (EMD). Results show that EMD helps to improve forecasting results.  ...  This research utilizes several well known forecasting techniques in combination with EMD to investigate the tradeoffs of EMD's decomposition (sifting) step for forecasting the arrival workload of an enterprise  ...  Acknowledgment The authors would like to thank Denny Brewer for providing feedbacks and the workload traces used in this research.  ... 
doi:10.1109/itng.2012.36 dblp:conf/itng/NgoAH12 fatcat:wkcojajirvaebjhpq4lww4o6rm

Attention-Based STL-BiLSTM Network to Forecast Tourist Arrival

Mohd Adil, Jei-Zheng Wu, Ripon K. Chakrabortty, Ahmad Alahmadi, Mohd Faizan Ansari, Michael J. Ryan
2021 Processes  
A seasonal and trend decomposition using the Loess (STL) approach is utilized to decompose time series tourist arrival data suggested by previous studies.  ...  In this study, we propose a bidirectional long short-term memory (BiLSTM) neural network to forecast the arrival of tourists along with SII indicators.  ...  Acknowledgments: The authors would like to thank Sondoss El Sawah, Acting Director, Centre for System Capability, UNSW-Canberra at ADFA for her critical evaluation, numerous discussions, and helpful comments  ... 
doi:10.3390/pr9101759 fatcat:ginygfjzzzhbpm4yctfoafitpm

Hybrid Transformer Network for Different Horizons-based Enriched Wind Speed Forecasting [article]

Dr. M. Madhiarasan, Prof. Partha Pratim Roy
2022 arXiv   pre-print
The proposed hybrid forecasting model decomposes the original wind speed data into IMFs (Intrinsic Mode Function) using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN  ...  wind speed forecasting than state-of-the-art methods and reduces the burden on the power system engineer.  ...  [16] studied short-term wind speed forecasting using ANN and SVR (Support Vector Regression) in association with various variants of EMD (Empirical Mode Decomposition).  ... 
arXiv:2204.09019v1 fatcat:3hhi2mszhfeela3so4a26vxele

Tensor-based anomaly detection: An interdisciplinary survey

Hadi Fanaee-T, João Gama
2016 Knowledge-Based Systems  
This survey aims to highlight the potential of tensor-based techniques as a novel approach for detection and identification of abnormalities and failures.  ...  Traditional spectral-based methods such as PCA are popular for anomaly detection in a variety of problems and domains.  ...  In the majority of these applications, the typical tensor is a three-order tensor of I (batch) × J (measurement) × K (time) which usually is unfolded in batch or time mode.  ... 
doi:10.1016/j.knosys.2016.01.027 fatcat:lejxxae63jcutfx2ncahownt7e

Short-Term Passenger Flow Prediction with Decomposition in Urban Railway Systems

Yangyang Zhao, Zhenliang Ma, Yi Yang, Wenhua Jiang, Xinguo Jiang
2020 IEEE Access  
This paper proposes a hybrid prediction model with time series decomposition and explores its performance for different types of passenger flows with varied characteristics in urban railway systems.  ...  Accurate prediction of short-term passenger flow is vital for real-time operations control and management.  ...  [8] used empirical mode decomposition (EMD) to obtain the intrinsic mode function (IMF) components of passenger flow data and identified the useful IMFs as inputs for the back-propagation neural network  ... 
doi:10.1109/access.2020.3000242 fatcat:jmm7ag6wwzdq3gjnqc4o7oybpm

ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting [article]

Slawek Smyl, Grzegorz Dudek, Paweł Pełka
2021 arXiv   pre-print
The empirical study clearly shows that the proposed model has high expressive power to solve nonlinear stochastic forecasting problems with TS including multiple seasonality and significant random fluctuations  ...  Short-term load forecasting (STLF) is challenging due to complex time series (TS) which express three seasonal patterns and a nonlinear trend.  ...  26] an empirical mode STLF literature in various ways.  ... 
arXiv:2112.02663v1 fatcat:e3n334d5ezhbpd4g5zqovufa3i

Self-adaptive workload classification and forecasting for proactive resource provisioning

Nikolas Roman Herbst, Nikolaus Huber, Samuel Kounev, Erich Amrehn
2013 Proceedings of the ACM/SPEC international conference on International conference on performance engineering - ICPE '13  
In a case study, between 55% and 75% of the violations of a given service level objective can be prevented by applying proactive resource provisioning based on the forecast results of our implementation  ...  In several experiments and case studies based on real-world workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time.  ...  For an arrival rate lower than a given threshold, the running server instances are not efficiently used 23 and therefore one of them is shut down or put in stand by mode.  ... 
doi:10.1145/2479871.2479899 dblp:conf/wosp/HerbstHKA13 fatcat:e42wnesmdbcgzj7ygvllbvedx4

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

Wei CHEN, Jian SUN, Shangce GAO, Jiu-Jun CHENG, Jiahai WANG, Yuki TODO
2017 IEICE transactions on information and systems  
In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting.  ...  Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered  ...  The reasons that the batch mode is selected are manifold: (1) it requires less weight update and provides a more accurate measurement of the required weight changes [56] ; (2) the batch mode requires  ... 
doi:10.1587/transinf.2016edp7152 fatcat:k6nzk4a2lnd6xgzlqz6vylz324

On the use of hybrid reinforcement learning for autonomic resource allocation

Gerald Tesauro, Nicholas K. Jong, Rajarshi Das, Mohamed N. Bennani
2007 Cluster Computing  
The present work shows how to combine the strengths of both RL and queuing models in a hybrid approach, in which RL trains offline on data collected while a queuing model policy controls the system.  ...  Reinforcement Learning (RL) provides a promising new approach to systems performance management that differs radically from standard queuing-theoretic approaches making use of explicit system performance  ...  in the means of the actual and predicted response times is less than 20% for a wide range of arrival rates and server allocations.  ... 
doi:10.1007/s10586-007-0035-6 fatcat:kph55kyguvgztatvvdmk2rzbrq

Complex Theory and Batch Processing in Mechanical Systemic Data Extraction

Xuefang Chang, Hongxia Pan, Jian Xu, Sha Qiao, Tong Wang
2022 IEEE Access  
The accuracy of fault classification and forecast of the eigenvalue extracted from the Automatic Batch Reading File (ABRF) and the Ensemble Empirical Mode Decomposition (EEMD) method is improved by 9%  ...  This paper designs a new batching program to extract the original data, which helps to traverse the entire sample space quickly and provides a new approach for data extraction based on the motion stroke  ...  ACKNOWLEDGMENT The authors would like to thank all the reviewers who participated in the review and MJEditor (www.mjeditor.com) for their linguistic assistance during the preparation of this manuscript  ... 
doi:10.1109/access.2022.3178790 fatcat:a3wp6lgj3rhr5a5yuvcwthkxhy

An Ensemble Model to Minimize Fluctuation Influences on Short-Term Medical Workload Prediction

Tasquia Mizan, Sharareh Taghipour
2022 Scientia Iranica. International Journal of Science and Technology  
The execution time of a machine learning model is also crucial when it is deployed in a real-time environment.  ...  A meta-model is developed in the online forecasting phase which ensures faster execution for incoming data.  ...  Availability of data and material: The data that supports the findings of this study was made available by Intelerad Medical Systems as part of NSERC (EGP524631-2018) collaboration, and its availability  ... 
doi:10.24200/sci.2022.58458.5733 fatcat:7qcc3ni2kjf4tj6cvneooknohq

A Hybrid Simulation Model for Open Software Development Processes [article]

Razieh Saremi
2021 arXiv   pre-print
The resource supply is a pool of unknown workers who work from different location and time zone and are interested in performing various type of tasks.  ...  Therefore, to ensure effectiveness of open software development, there is a need for improved understanding and visibility into characteristics associated with attracting reliable workers in making qualified  ...  Based on the available empirical data, for large projects arriving on average 90 parallel tasks at the same period of time, may lead us to average of 86% success.  ... 
arXiv:2107.07485v1 fatcat:6l23o5p5cbdnlcklmbzahvjr3i

Improved best estimate plus uncertainty methodology, including advanced validation concepts, to license evolving nuclear reactors

C. Unal, B. Williams, F. Hemez, S.H. Atamturktur, P. McClure
2011 Nuclear Engineering and Design  
These might include the extension of the M&S capabilities for application to full-scale systems.  ...  Historically, the role of experiments has been as a primary tool for the design and understanding of nuclear system behavior, while M&S played the subordinate role of supporting experiments.  ...  and scale-up capability and to specify ranges of parameter variations needed for sensitivity studies.  ... 
doi:10.1016/j.nucengdes.2011.01.048 fatcat:s5hpacwfpfdxfgqpfppjv4i4ue

Flood Forecasting in Large River Basins Using FOSS Tool and HPC

Upasana Dutta, Yogesh Kumar Singh, T. S. Murugesh Prabhu, Girishchandra Yendargaye, Rohini Gopinath Kale, Binay Kumar, Manoj Khare, Rahul Yadav, Ritesh Khattar, Sushant Kumar Samal
2021 Water  
The system shows good accuracy and better lead time suitable for flood forecasting in near-real-time.  ...  With increased incidences of floods and their related catastrophes, the design, development, and deployment of an Early Warning System for Flood Prediction (EWS-FP) for the river basins of India is needed  ...  Acknowledgments: The authors sincerely acknowledge the Ministry of Electronics and Information Technology (MeitY), and Deprtment fo Sciece and Technology (DST), Government of India for the funding support  ... 
doi:10.3390/w13243484 fatcat:pjqxqojaafgj7gt6xpqtprz3qq
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