Clothing Sale Forecasting by a Composite GRU–Prophet Model With an Attention Mechanism

Li, Yuanjiang; Yi, Yang; Zhu, Kai; Zhang, Jinglin

doi:10.1109/tii.2021.3057922

Cited by 56 publications

(19 citation statements)

References 28 publications

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“…The attention mechanism has been widely applied in industrial applications to improve performance. Li et al utilize GRU (an RNN variant) with an attention mechanism for sales forecasting in [47]. Wang et al designed CNN with attention to machinery prognostics in [48].…”

Section: Deep Neural Network Architecturesmentioning

confidence: 99%

A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions

Kovatsch

Mattern

et al. 2022

Applied Sciences

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Artificial intelligence (AI) has been successfully applied in industry for decades, ranging from the emergence of expert systems in the 1960s to the wide popularity of deep learning today. In particular, inexpensive computing and storage infrastructures have moved data-driven AI methods into the spotlight to aid the increasingly complex manufacturing processes. Despite the recent proverbial hype, however, there still exist non-negligible challenges when applying AI to smart manufacturing applications. As far as we know, there exists no work in the literature that summarizes and reviews the related works for these challenges. This paper provides an executive summary on AI techniques for non-experts with a focus on deep learning and then discusses the open issues around data quality, data secrecy, and AI safety that are significant for fully automated industrial AI systems. For each challenge, we present the state-of-the-art techniques that provide promising building blocks for holistic industrial AI solutions and the respective industrial use cases from several domains in order to better provide a concrete view of these techniques. All the examples we reviewed were published in the recent ten years. We hope this paper can provide the readers with a reference for further studying the related problems.

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Section: Deep Neural Network Architecturesmentioning

confidence: 99%

A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions

Kovatsch

Mattern

et al. 2022

Applied Sciences

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“…2. The GRU accepts the hidden state output ht-1 at the previous moment and the current input xt, obtaining the current hidden state output ht through a series of nonlinear processing in the reset gate function and update gate function [28]. The calculation procedure of the forward pass in a GRU architecture can be formulated by the ( 3)-( 6).…”

Section: Gru Modulementioning

confidence: 99%

CNN-GRU-Attention Based Short-Term Load Forecasting of Distribution Networks

Qin

Gao

et al. 2022

Preprint

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Optimising short-term load forecasting performance is a challenge due to the randomness of nonlinear power load and variability of system operation mode. The existing methods generally ignore how to reasonably and effectively combine the complementary advantages among them and fail to capture enough internal information from load sequence, resulting in accuracy reduction. To achieve accurate and efficient short-term load forecasting, an integral implementation framework is proposed based on convolutional neural network (CNN), gated recurrent unit (GRU) and channel attention mechanism. CNN and GRU are first combined to fully extract the complicated dynamic features and learn the time compliance relationship of load sequence. Based on CNN-GRU network, the channel attention mechanism is introduced to further reduce the loss of historical information and enhance the impact of important features. Then, the overall framework of short-term load forecasting based on CNN-GRU-Attention network is proposed, and the coupling relationship between each designed stage is revealed. Finally, the developed framework is implemented on one realistic load dataset of distribution networks, and the experimental results verify the proposed method outperforms the state-of-the-art models in common metrics.

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“…Typically, load forecasting methods are divided into statistical-based and machine learning-based forecasting methods [4][5][6][7]. Statistical methods include multiple linear regression [8], autoregressive moving average [9], and autoregression [10,11]. These methods have a distinctive feature with faster calculation speed.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network

Cai

et al. 2022

Applied Sciences

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With the continuous increase in user-side flexible controllable resources connected into a distribution system, the components of electrical load become too diverse and difficult to be accuracy forecasted. A short-term load forecast method that integrates variational modal decomposition (VMD), gated recurrent unit (GRU) and time convolutional network (TCN) into a hybrid network is proposed in this paper. Firstly, original electrical load sequence data with noise are decomposed into intrinsic IMF components with different frequencies and amplitudes based on the VMD method. Secondly, a combined load forecasting method based on the GRU and TCN network is proposed for the high and low-frequency load subsequent signals, respectively. Finally, the high and low-frequency signals forecasting results of the GRU and TCN network are rebuilt for the final load forecasting. The experiment results based on actual operation data (data set 1) and simulation data (data set 2), which show that the proposed method can reduce the forecasting error by 36.20% and 10.8%, respectively, in comparison with VMD-GRU. The reliability and accuracy of the proposed method is verified through the comparison with other methods such as LSTM, Prophet and XG Boost.

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Clothing Sale Forecasting by a Composite GRU–Prophet Model With an Attention Mechanism

Cited by 56 publications

References 28 publications

A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions

A Review on AI for Smart Manufacturing: Deep Learning Challenges and Solutions

CNN-GRU-Attention Based Short-Term Load Forecasting of Distribution Networks

Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network

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