A Gated Dilated Causal Convolution Based Encoder-Decoder for Network Traffic Forecasting

Zhang, Xin; You, Jinmao

doi:10.1109/access.2019.2963449

Cited by 21 publications

(5 citation statements)

References 23 publications

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“…Causal convolution [26] serves two essential functions in TCN [27]: ensuring that the network generates an output of the same length as the input and preventing leakage from future to past. Causal convolution differs from a standard convolutional neural network in that it utilizes a unidirectional structure, ensuring that the model input and output are the same size.…”

Section: Causal Convolutionmentioning

confidence: 99%

Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network

Zhang,

Sun

et al. 2023

Applied Sciences

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Wind power generation is a renewable energy source, and its power output is influenced by multiple factors such as wind speed, direction, meteorological conditions, and the characteristics of wind turbines. Therefore, accurately predicting wind power is crucial for the grid operation and maintenance management of wind power plants. This paper proposes a hybrid model to improve the accuracy of wind power prediction. Accurate wind power forecasting is critical for the safe operation of power systems. To improve the accuracy of wind power prediction, this paper proposes a hybrid model incorporating variational modal decomposition (VMD), a Sparrow Search Algorithm (SSA), and a temporal-convolutional-network-based bi-directional gated recurrent unit (TCN-BiGRU). The model first uses VMD to break down the raw power data into several modal components, and then it builds an SSA-TCN-BIGRU model for each component for prediction, and finally, it accumulates all the predicted components to obtain the wind power prediction results. The proposed short-term wind power prediction model was validated using measured data from a wind farm in China. The proposed VMD-SSA-TCN-BiGRU forecasting framework is compared with benchmark models to verify its practicability and reliability. Compared with the TCN-BiGRU, the symmetric mean absolute percentage error, the mean absolute error, and the root mean square error of the VMD-SSA-TCN-BiGRU model reduced by 34.36%, 49.14%, and 55.94%.

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Section: Causal Convolutionmentioning

confidence: 99%

Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network

Zhang,

Sun

et al. 2023

Applied Sciences

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“…This formula is simple to categorize for multi-dimension signals, but for the sake of brevity, it will not be included it here. To guarantee length continuity, padding (zero as well as recreate) to the dimension of k − 1 is added to a left tail of a transmitter [26]. To give so every component a larger receptive ground, we combined several causal convolution layers.…”

Section: Dilated Causal Convolutionmentioning

confidence: 99%

Spatial and Temporal Normalization for Multi-Variate Time Series Prediction Using Machine Learning Algorithms

et al. 2022

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Multi-variable time series (MTS) information is a typical type of data inference in the real world. Every instance of MTS is produced via a hybrid dynamical scheme, the dynamics of which are often unknown. The hybrid species of this dynamical service are the outcome of high-frequency and low-frequency external impacts, as well as global and local spatial impacts. These influences impact MTS’s future growth; hence, they must be incorporated into time series forecasts. Two types of normalization modules, temporal and spatial normalization, are recommended to accomplish this. Each boosts the original data’s local and high-frequency processes distinctly. In addition, all components are easily incorporated into well-known deep learning techniques, such as Wavenet and Transformer. However, existing methodologies have inherent limitations when it comes to isolating the variables produced by each sort of influence from the real data. Consequently, the study encompasses conventional neural networks, such as the multi-layer perceptron (MLP), complex deep learning methods such as LSTM, two recurrent neural networks, support vector machines (SVM), and their application for regression, XGBoost, and others. Extensive experimental work on three datasets shows that the effectiveness of canonical frameworks could be greatly improved by adding more normalization components to how the MTS is used. This would make it as effective as the best MTS designs are currently available. Recurrent models, such as LSTM and RNN, attempt to recognize the temporal variability in the data; however, as a result, their effectiveness might soon decline. Last but not least, it is claimed that training a temporal framework that utilizes recurrence-based methods such as RNN and LSTM approaches is challenging and expensive, while the MLP network structure outperformed other models in terms of time series predictive performance.

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“…44 LSTM as an extension of RNN is proposed to resolve the vulnerability of normal RNNs against the gradient exploding/vanishing problem caused by long-term dependencies. [45][46][47] LSTM with some innovations in its architecture including a triple gate mechanism to control inputs to cells, and a feedback loop for data retention, can learn long-term dependencies and remove invalid inputs that cause perturbation in cell's outputs. 6,48 In practice, an implemented LSTM model usually consists of a set of blocks where each block contains several LSTM cells.…”

Section: Techniques For Ntpmentioning

confidence: 99%

“…Different RNN‐based architectures can be defined as per adopted activation function and how the neurons connect to each other, namely that of Fully Recurrent Neural Network (FRNN), Bidirectional Neural Networks (BNN), stochastic neural networks, and the well‐known paradigm Long short‐term memory (LSTM) 44 . LSTM as an extension of RNN is proposed to resolve the vulnerability of normal RNNs against the gradient exploding/vanishing problem caused by long‐term dependencies 45‐47 . LSTM with some innovations in its architecture including a triple gate mechanism to control inputs to cells, and a feedback loop for data retention, can learn long‐term dependencies and remove invalid inputs that cause perturbation in cell's outputs 6,48 .…”

Section: Basic Conceptsmentioning

confidence: 99%

From statistical‐ to machine learning‐based network traffic prediction

Lohrasbinasab

Shahraki

Taherkordi

et al. 2021

Trans Emerging Tel Tech

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Nowadays, due to the exponential and continuous expansion of new paradigms such as Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a tremendous and sharp increase of network traffic. In such large‐scale, heterogeneous, and complex networks, the volume of transferred data, as big data, is considered a challenge causing different networking inefficiencies. To overcome these challenges, various techniques are introduced to monitor the performance of networks, called Network Traffic Monitoring and Analysis (NTMA). Network Traffic Prediction (NTP) is a significant subfield of NTMA which is mainly focused on predicting the future of network load and its behavior. NTP techniques can generally be realized in two ways, that is, statistical‐ and Machine Learning (ML)‐based. In this paper, we provide a study on existing NTP techniques through reviewing, investigating, and classifying the recent relevant works conducted in this field. Additionally, we discuss the challenges and future directions of NTP showing that how ML and statistical techniques can be used to solve challenges of NTP.

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A Gated Dilated Causal Convolution Based Encoder-Decoder for Network Traffic Forecasting

Cited by 21 publications

References 23 publications

Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network

Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network

Spatial and Temporal Normalization for Multi-Variate Time Series Prediction Using Machine Learning Algorithms

From statistical‐ to machine learning‐based network traffic prediction

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