Building Trend Fuzzy Granulation-Based LSTM Recurrent Neural Network for Long-Term Time-Series Forecasting

Tang, Yuqing; Yu, Feihong; Pedrycz, Witold; Yang, Xiyang; Wang, Jiayin; Liu, Shihu

doi:10.1109/tfuzz.2021.3062723

Cited by 52 publications

(17 citation statements)

References 37 publications

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“…Second, the LSTM model is created. Setting parameters: the number of input neurons, output neurons, hidden neurons, learning rate, batch size, epoch size (i.e., the number of training cycles) and the number of LSTM layers [ 29 , 30 ]. The loss error is chosen as the mean square error, and the LSTM neural network is trained using the Adam optimisation technique [ 31 ].…”

Section: Prediction Based On Lstm Modelmentioning

confidence: 99%

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AI Based Digital Twin Model for Cattle Caring

Han¹,

Lin²

2022

Sensors

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In this paper, we develop innovative digital twins of cattle status that are powered by artificial intelligence (AI). The work is built on a farm IoT system that remotely monitors and tracks the state of cattle. A digital twin model of cattle based on Deep Learning (DL) is generated using the sensor data acquired from the farm IoT system. The physiological cycle of cattle can be monitored in real time, and the state of the next physiological cycle of cattle can be anticipated using this model. The basis of this work is the vast amount of data that is required to validate the legitimacy of the digital twins model. In terms of behavioural state, this digital twin model has high accuracy, and the loss error of training reach about 0.580 and the loss error of predicting the next behaviour state of cattle is about 5.197 after optimization. The digital twins model developed in this work can be used to forecast the cattle’s future time budget.

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Section: Prediction Based On Lstm Modelmentioning

confidence: 99%

“… Time series t : The sequence number of this hour (e.g., 0:00 a.m. on the first day is the first hour, and t is 1. So on, 0:00am on the second day is the 25th h, and t is 25) [ 30 , 32 ]. Training: Both the input and output data are periodicities.…”

Section: Prediction Based On Lstm Modelmentioning

confidence: 99%

AI Based Digital Twin Model for Cattle Caring

Han¹,

Lin²

2022

Sensors

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“…High, medium, and low attention are indicated by the colors red, white, and green, respectively. Note that the attention is not necessarily synced with correlation, as the former more likely represents the model's assessment of causality between variables (Wang X. et al, 2021;Yang et al, 2021).…”

Section: Cross-entity Relationship Evaluationmentioning

confidence: 99%

“…Recurrent neural networks (RNN) (Rumelhart and McClelland, 1987) is a category of neural network suitable for time-series modeling. RNNs use hidden states that are iteratively supplied back to the network for temporary timerelated information representation and storage, as implied by the name (Tang et al, 2021). This gives the model memory for temporal properties.…”

Section: Introductionmentioning

confidence: 99%

A Transformer-Based Multi-Entity Load Forecasting Method for Integrated Energy Systems

Wang

Zhu²,

Xiao³

et al. 2022

Front. Energy Res.

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Energy load forecasting is a critical component of energy system scheduling and optimization. This method, which is classified as a time-series forecasting method, uses prior features as inputs to forecast future energy loads. Unlike a traditional single-target scenario, an integrated energy system has a hierarchy of many correlated energy consumption entities as prediction targets. Existing data-driven approaches typically interpret entity indexes as suggestive features, which fail to adequately represent interrelationships among entities. This paper, therefore, proposes a neural network model named Cross-entity Temporal Fusion Transformer (CETFT) that leverages a cross-entity attention mechanism to model inter-entity correlations. The enhanced attention module is capable of mapping the relationships among entities within a time window and informing the decoder about which entity in the encoder to concentrate on. In order to reduce the computational complexity, shared variable selection networks are adapted to extract features from different entities. A data set obtained from 13 buildings on a university campus is used as a case study to verify the performance of the proposed approach. Compared to the comparative methods, the proposed model achieves the smallest error on most horizons and buildings. Furthermore, variable importance, temporal correlations, building relationships, and time-series patterns in data are analyzed with the attention mechanism and variable selection networks, therefore the rich interpretability of the model is verified.

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“…Presented results show that this type of neural networks can well adapt to variety of examples. In [19] was discussed efficiency of RNN in stochastic time series analysis, while results of [16] show solution to improve long time series processing with fuzzy granulation.…”

Section: Introductionmentioning

confidence: 99%

Recurrent neural network model for high-speed train vibration prediction from time series

Siłka

Wieczorek

Woźniak

2022

Neural Comput & Applic

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In this article, we want to discuss the use of deep learning model to predict potential vibrations of high-speed trains. In our research, we have tested and developed deep learning model to predict potential vibrations from time series of recorded vibrations during travel. We have tested various training models, different time steps and potential error margins to examine how well we are able to predict situation on the track. Summarizing, in our article we have used the RNN-LSTM neural network model with hyperbolic tangent in hidden layers and rectified linear unit gate at the final layer in order to predict future values from the time series data. Results of our research show the our system is able to predict vibrations with Accuracy of above 99% in series of values forward.

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Building Trend Fuzzy Granulation-Based LSTM Recurrent Neural Network for Long-Term Time-Series Forecasting

Cited by 52 publications

References 37 publications

AI Based Digital Twin Model for Cattle Caring

AI Based Digital Twin Model for Cattle Caring

A Transformer-Based Multi-Entity Load Forecasting Method for Integrated Energy Systems

Recurrent neural network model for high-speed train vibration prediction from time series

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