A Multivariate Fuzzy Time Series Resource Forecast Model for Clouds using LSTM and Data Correlation Analysis

Tran, Nhuan; Nguyen, Thang; Nguyen, Binh Minh; Nguyen, Giang

doi:10.1016/j.procs.2018.07.298

Cited by 48 publications

(19 citation statements)

References 8 publications

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“…A forecast model for proactive cloud auto-scaling models combining with few mechanisms was built in [24]. Multivariate Fuzzy-LSTM (MF-LSTM) was modeled for the forecast of consumption of resources with data of multivariate time series simultaneously.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

2019

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Prediction of rainfall is one of the major concerns in the domain of meteorology. Several techniques have been formerly proposed to predict rainfall based on statistical analysis, machine learning and deep learning techniques. Prediction of time series data in meteorology can assist in decision-making processes carried out by organizations responsible for the prevention of disasters. This paper presents Intensified Long Short-Term Memory (Intensified LSTM) based Recurrent Neural Network (RNN) to predict rainfall. The neural network is trained and tested using a standard dataset of rainfall. The trained network will produce predicted attribute of rainfall. The parameters considered for the evaluation of the performance and the efficiency of the proposed rainfall prediction model are Root Mean Square Error (RMSE), accuracy, number of epochs, loss, and learning rate of the network. The results obtained are compared with Holt–Winters, Extreme Learning Machine (ELM), Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network and Long Short-Term Memory models in order to exemplify the improvement in the ability to predict rainfall.

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Section: Methodsmentioning

confidence: 99%

Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

2019

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“…It also provides more consistency in predictions over time in comparison to ML models such as decision tree or SVM [47]. LSTMs come in many variances such as gated recurrent unit (GRU), bidirectional LSTM [48], attention LSTM [49] and stacked variances that promise model quality improvements [50]- [52] at the cost of model complexity and consequently higher requirement of computational power.…”

Section: B Proactive Forecasting and Deep Learningmentioning

confidence: 99%

Deep Learning for Proactive Network Monitoring and Security Protection

et al. 2020

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The work presented in this paper deals with a proactive network monitoring for security and protection of computing infrastructures. We provide an exploitation of an intelligent module, in the form of a as a machine learning application using deep learning modeling, in order to enhance functionality of intrusion detection system supervising network traffic flows. Currently, intrusion detection systems work well for network monitoring in near real-time and they effectively deal with threats in a reactive way. Deep learning is the emerging generation of artificial intelligence techniques and one of the most promising candidates for intelligence integration into traditional solutions leading to quality improvement of the original solutions. The work presented in this paper faces the challenge of cooperation between deep learning techniques and large-scale data processing. The outcomes obtained from extensive and careful experiments show the applicability and feasibility of simultaneously modelled multiple monitoring channels using deep learning techniques. The proper joining of deep learning modelling with scalable data preprocessing ensures high quality and stability of model performance in dynamic and fast-changing environments such as network traffic flow monitoring.

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“…This extra information has been selected considering the mutual information (MI) and correlation between the BSM1 concentrations entering in the fifth reactor tank and the controlled variable. Thus, those measurements showing the highest correlation and MI with the controlled signal will be the ones considered to complement the LSTM input data [ 48 , 49 ]. Results in Figure 5 show that among the 15 possible measurements given by the BSM1 model [ 26 ], the dissolved oxygen (

), the nitrate-nitrogen (

) and the ammonium (

) are the concentrations yielding the highest MI with the

concentration.…”

Section: Denoising Lstm-based Imcmentioning

confidence: 99%

Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments—The Wastewater Treatment Plant Control Case

Pisa

Morell

Vicario

et al. 2020

Sensors

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The evolution of industry towards the Industry 4.0 paradigm has become a reality where different data-driven methods are adopted to support industrial processes. One of them corresponds to Artificial Neural Networks (ANNs), which are able to model highly complex and non-linear processes. This motivates their adoption as part of new data-driven based control strategies. The ANN-based Internal Model Controller (ANN-based IMC) is an example which takes advantage of the ANNs characteristics by modelling the direct and inverse relationships of the process under control with them. This approach has been implemented in Wastewater Treatment Plants (WWTP), where results show a significant improvement on control performance metrics with respect to (w.r.t.) the WWTP default control strategy. However, this structure is very sensible to non-desired effects in the measurements—when a real scenario showing noise-corrupted data is considered, the control performance drops. To solve this, a new ANN-based IMC approach is designed with a two-fold objective, improve the control performance and denoise the noise-corrupted measurements to reduce the performance degradation. Results show that the proposed structure improves the control metrics, (the Integrated Absolute Error (IAE) and the Integrated Squared Error (ISE)), around a 21.25% and a 54.64%, respectively.

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A Multivariate Fuzzy Time Series Resource Forecast Model for Clouds using LSTM and Data Correlation Analysis

Cited by 48 publications

References 8 publications

Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units

Deep Learning for Proactive Network Monitoring and Security Protection

Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments—The Wastewater Treatment Plant Control Case

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