Online reliability time series prediction via convolutional neural network and long short term memory for service-oriented systems

Wang, Hongbing; Yang, Zhou; Yu, Qi; Hong, Tianjing; Lin, Xin

doi:10.1016/j.knosys.2018.07.006

Cited by 47 publications

(21 citation statements)

References 23 publications

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“…Poor performance was achieved by the model in 30-min interval data sets, probably because the LSTM network was not adequately trained and also few data sets were recorded. Wang et al employed LSTM to forecast the reliability of the server system and found that the prediction accuracy of one month's data set is higher than that of the 24-h data set, indicating that more training data has a positive impact on the prediction accuracy of the model [60]. Previous studies have developed many classic models of noise prediction, which attained good performance.…”

Section: Improvement Of Prediction Methods Based On Neural Networkmentioning

confidence: 99%

Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network

2020

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Noise pollution is one of the major urban environmental pollutions, and it is increasingly becoming a matter of crucial public concern. Monitoring and predicting environmental noise are of great significance for the prevention and control of noise pollution. With the advent of the Internet of Things (IoT) technology, urban noise monitoring is emerging in the direction of a small interval, long time, and large data amount, which is difficult to model and predict with traditional methods. In this study, an IoT-based noise monitoring system was deployed to acquire the environmental noise data, and a two-layer long short-term memory (LSTM) network was proposed for the prediction of environmental noise under the condition of large data volume. The optimal hyperparameters were selected through testing, and the raw data sets were processed. The urban environmental noise was predicted at time intervals of 1 s, 1 min, 10 min, and 30 min, and their performances were compared with three classic predictive models: random walk (RW), stacked autoencoder (SAE), and support vector machine (SVM). The proposed model outperforms the other three existing classic methods. The time interval of the data set has a close connection with the performance of all models. The results revealed that the LSTM network could reflect changes in noise levels within one day and has good prediction accuracy. Impacts of monitoring point location on prediction results and recommendations for environmental noise management were also discussed in this paper.

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Section: Improvement Of Prediction Methods Based On Neural Networkmentioning

confidence: 99%

Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network

2020

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“…LSTM is an advanced and deep architecture of recurrent neural network (RNN) [19]. RNN can handle time series data because the activation of a recurrent hidden state at each time step depends on the hidden state of a previous time step, whereas conventional neural network transmits information to the next layer without reference to the previous time step [20,21]. As illustrated in Figure 1, t, which signifies a hidden state at each time, is updated at each time step by the input as well as the hidden state at time t and t -1, respectively.…”

Section: Long Short-term Memorymentioning

confidence: 99%

A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

Son

Kim

2020

Sustainability

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Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.

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“…To improve the prediction accuracy, Wang et al employed motifsbased dynamic Bayesian networks to estimate the web service's reliability in the near future [18]. e prediction method was further improved by long short-term memory network and self-adaption using data from more nearby time points [19]. As mentioned above, the above researches leveraged historical web service usage data from a single user to construct online prediction rules yet ignored the similarities between the service usage data from multiple users on the same service.…”

Section: Related Workmentioning

confidence: 99%

“…Following the quality time series definition in [19], let q i denote the service quality, either the response time or the throughput value, in the i-th time point. e corresponding runtime quality time series measured during Δt h is denoted as q h , which is a vector describing the quality value in n time points:…”

Section: Problem Formulationmentioning

confidence: 99%

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Runtime Quality Prediction for Web Services via Multivariate Long Short‐Term Memory

Guo

Wan

et al. 2019

Mathematical Problems in Engineering

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Online quality prediction helps to identify the web service quality degradation in the near future. While historical web service usage data are used for online prediction in preventive maintenance, the similarities in the usage data from multiple users invoking the same web service are ignored. To improve the service quality prediction accuracy, a multivariate time series model is built considering multiple user invocation processes. After analysing the cross-correlation and similarity of the historical web service quality data from different users, the time series model is estimated using the multivariate LSTM network and used to predict the quality data for the next few time series points. Experiments were conducted to compare the multivariate methods with the univariate methods. The results showed that the multivariate LSTM model outperformed the univariate models in both MAE and RMSE and achieved the best performance in most test cases, which proved the efficiency of our method.

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Online reliability time series prediction via convolutional neural network and long short term memory for service-oriented systems

Cited by 47 publications

References 23 publications

Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network

Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network

A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

Runtime Quality Prediction for Web Services via Multivariate Long Short‐Term Memory

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