DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction

Liu, Yeqi; Gong, Chuanyang; Yang, Ling; Chen, Yingyi

doi:10.1016/j.eswa.2019.113082

Cited by 221 publications

(86 citation statements)

References 34 publications

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“…For instance, house buildings with small surface and roof areas will require little amount of energy to obtain optimal heating and cooling temperature. For instance, in, 85 Tran Nguyen et al [101] Rasel et al [102] Do et al [103] Zhang et al [104] Li et al [105] Adams et al [106] Electricity Load Diagrams [67] Amri et al [107] Moslemi et al [108] Jin et al [109] --Premarathne et al [110] Hosseini et al [111] Ertugrul et al [31] Moslemi et al [108] Sen et al [113] Fenza et al [114] Bicego et al [115] Chen et al [116] SML2010 [120] Bevilacqua et al [118] Zamora-Martínez et al [117] Maggiolo et al [120] --Qin et al [121] Wollsen et al [122] Li et al [123] Liu et al [124] Combined Cycle Power Plant Dataset [126] Janoušek et al [126] Rashid et al [127] --Sun et al [128] Roni et al [129] Elfaki et al [130] Cao et al [131] 2006 and November 2010. There are some missing parameters (about 1.25%) in the dataset that are represented by the absence of values between two consecutive semi-colon attribute separators.…”

Section: Energy Efficiencymentioning

confidence: 99%

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

et al. 2021

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Section: Energy Efficiencymentioning

confidence: 99%

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

et al. 2021

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“…Prediction chooses one of ten classes with a specific interval. Past research used multivariates for rainfall prediction [28] with RNN and LSTM. Sometimes, climate data has missing or lost observation.…”

Section: Precipitation Prediction Modelmentioning

confidence: 99%

Precipitation prediction using recurrent neural networks and long short-term memory

Priatna

Djamal

2020

TELKOMNIKA

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Prediction of meteorological variables such as precipitation, temperature, wind speed, and solar radiation is beneficial for human life. The variable observations data is available from time to time for more than thirty years, scattered each observation station makes the opportunity to map patterns into predictions. However, the complexity of weather variables is very high, one of which is influenced by Decadal phenomena such as El-Nino Southern Oscillation and IOD. Weather predictions can be reviewed for the duration, prediction variables, and observation stations. This research proposed precipitation prediction using recurrent neural networks and long short-term memory. Experiments were carried out using the prediction duration factor, the period as a feature and the amount of data set used, and the optimization model. The results showed that the time-lapse as a shorter feature gives good accuracy. Also, the duration of weekly predictions provides more accuracy than monthly, which is 85.71% compared to 83.33% of the validation data.

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“…Prediction. Liu et al combined the dual stage two-phase model with the feedback neural network to make a longterm prediction of multiple time series in [28]. And it has been successfully used to develop widely used expert or intelligent systems.…”

Section: Application Of Feedback Neural Network In Time-seriesmentioning

confidence: 99%

Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network

Ling

2020

Complexity

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In target estimating sea clutter or actual mechanical fault diagnosis, useful signal is often submerged in strong chaotic noise, and the targeted signal data are difficult to recover. Traditional schemes, such as Elman neural network (ENN), backpropagation neural network (BPNN), support vector machine (SVM), and multilayer perceptron- (MLP-) based model, are insufficient to extract the weak signal embedded in a chaotic background. To improve the estimating accuracy, a novel estimating method for aiming at extracting problem of weak pulse signal buried in a strong chaotic background is presented. Firstly, the proposed method obtains the vector sequence signal by reconstructing higher-dimensional phase space data matrix according to the Takens theorem. Then, a Jordan neural network- (JNN-) based model is designed, which can minimize the error squared sum by mixing the single-point jump model for targeting signal. Finally, based on short-term predictability of chaotic background, estimation of weak pulse signal from the chaotic background is achieved by a profile least square method for optimizing the proposed model parameters. The data generated by the Lorenz system are used as chaotic background noise for the simulation experiment. The simulation results show that Jordan neural network and profile least square algorithm are effective in estimating weak pulse signal from chaotic background. Compared with the traditional method, (1) the presented method can estimate the weak pulse signal in strong chaotic noise under lower error than ENN-based, BPNN-based, SVM-based, and -ased models and (2) the proposed method can extract the weak pulse signal under a higher output SNR than BPNN-based model.

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DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction

Cited by 221 publications

References 34 publications

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

Smart and intelligent energy monitoring systems: A comprehensive literature survey and future research guidelines

Precipitation prediction using recurrent neural networks and long short-term memory

Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network

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