Modelling email traffic workloads with RNN and LSTM models

Om, Khandu; Boukoros, Spyros; Nugaliyadde, Anupiya; McGill, Tanya; Dixon, Michael; Koutsakis, P.; Wong, Kok Wai

doi:10.1186/s13673-020-00242-w

Cited by 16 publications

(6 citation statements)

References 25 publications

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“…However, as the input time series information is too long, the effect of the RNN network is significantly reduced, and its ability to process long-term sequences is weak. At the same time, as the input sequence grows, the depth of the model also increases, and RNNs experience gradient vanishing and gradient exploding problems during training [5] . To solve the issues of simple RNNs, the Long Short-Term Memory (LSTM) model was proposed.…”

Section: Lstm Modelmentioning

confidence: 99%

Multi-direction prediction based on SALSTM model for ship motion

Xun,

Zhu,

Yang

et al. 2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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This paper proposes a self-attention LSTM (SALSTM) model for ship motion prediction, which combines the advantages of LSTM and self-attention mechanisms. The model also introduces the concept of attention gate. The paper studies the influence of forecast lead time on the prediction accuracy of three degrees of freedom: roll, surge and heave. The paper compares the SALSTM model with a baseline LSTM model on a ship motion data set under different forecast durations and lead times. The paper evaluates the performance of the SALSTM model using four metrics and verifies its effectiveness under three representative working conditions. The paper also gives the applicable conditions of the SALSTM model for ship motion prediction

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Section: Lstm Modelmentioning

confidence: 99%

Multi-direction prediction based on SALSTM model for ship motion

Xun,

Zhu,

Yang

et al. 2023

5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)

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“…ANNs are built primarily on the features extracted from input data. ANNs deal with the nonlinear relationships of time series data by learning from experience and building complex neural networks to generate optimal solutions for prediction problems [ 32 , 33 ]. ANN consists of input layer, zero or more hidden layers, and an output layer.…”

Section: Related Workmentioning

confidence: 99%

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network

Rouf

Malik

Sharma

et al. 2022

Computational Intelligence and Neuroscience

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The unprecedented Corona Virus Disease (COVID-19) pandemic has put the world in peril and shifted global landscape in unanticipated ways. The SARSCoV2 virus, which caused the COVID-19 outbreak, first appeared in Wuhan, Hubei Province, China, in December 2019 and quickly spread around the world. This pandemic is not only a global health crisis, but it has caused the major global economic depression. As soon as the virus spread, stock market prices plummeted and volatility increased. Predicting the market during this outbreak has been of substantial importance and is the primary motivation to carry out this work. Given the nonlinearity and dynamic nature of stock data, the prediction of stock market is a challenging task. The machine learning models have proven to be a good choice for the development of effective and efficient prediction systems. In recent years, the application of hyperparameter optimization techniques for the development of highly accurate models has increased significantly. In this study, a customized neural network model is proposed and the power of hyperparameter optimization in modelling stock index prices is explored. A novel dataset is generated using nine standard technical indicators and COVID-19 data. In addition, the primary focus is on the importance of selection of optimal features and their preprocessing. The utilization of multiple feature ranking techniques combined with extensive hyperparameter optimization procedures is comprehensive for the prediction of stock index prices. Moreover, the model is evaluated by comparing it with other models, and results indicate that the proposed model outperforms other models. Given the detailed design methodology, preprocessing, exploratory feature analysis, and hyperparameter optimization procedures, this work gives a significant contribution to stock analysis research community during this pandemic.

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“…Since the RNN model stores the corresponding historical information by the number of network layers, the less the number of network layers, the more incomplete the historical information recorded. In addition, the more the number of network layers, the more complex the training process will be, and it is easy to have the phenomenon that the gradient descent speed is fast or even disappears, both of which will lead to poor prediction performance of the model [25].…”

Section: Rnn and Lstm Te Earliest Known Rnn Is Hopfeldmentioning

confidence: 99%

Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT

Peng

Ding

2022

Discrete Dynamics in Nature and Society

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The high-speed development of mobile broadband networks and IoT applications has brought about massive data transmission and data processing, and severe traffic congestion has adversely affected the fast-growing networks and industries. To better allocate network resources and ensure the smooth operation of communications, predicting network traffic becomes an important tool. We investigate in detail the impact of variable sampling rate on traffic prediction and propose a high-speed traffic prediction method using machine learning and recurrent neural networks. We first investigate a VSR-NLMS adaptive prediction method to perform time series prediction dataset transformation. Then, we propose a VSR-LSTM algorithm for real-time prediction of network traffic. Finally, compared with the traditional traffic prediction algorithm based on fixed sampling rate (FSR-LSTM), we simulate the prediction accuracy of the VSR-LSTM algorithm based on the variable sampling rate proposed. The experiment shows that VSR-LSTM has higher traffic prediction accuracy because its sampling rate varies with the traffic.

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Modelling email traffic workloads with RNN and LSTM models

Cited by 16 publications

References 25 publications

Multi-direction prediction based on SALSTM model for ship motion

Multi-direction prediction based on SALSTM model for ship motion

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network

Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT

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