TADA: Trend Alignment with Dual-Attention Multi-task Recurrent Neural Networks for Sales Prediction

Chen, Tong; Yin, Hongzhi; Chen, Hongxu; Wu, Lin; Wang, Hao; Zhou, Xiaofang; Li, Xue

doi:10.1109/icdm.2018.00020

Cited by 69 publications

(56 citation statements)

References 26 publications

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“…The second attention is used to select relevant encoder hidden states across the time steps. Chen et al [23] presented a similar dual-attention model. The first attention extracts effective information from internal and external features while the second attention uses a sliding time step window to find the best matching pattern of the current predicted trend from the historical sequence.…”

Section: Attention Mechanisms In Sequential Deep Learningmentioning

confidence: 99%

Attention-Based Dual-Source Spatiotemporal Neural Network for Lightning Forecast

Lin

Geng

et al. 2019

IEEE Access

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Accurate lightning forecast is significant for disaster prevention and reduction. However, the mainstream lightning forecast methods, which mainly rely on numerical simulations and parameterizations, can hardly cope with the spatiotemporal deviations. Meanwhile, the rapid and complex evolution of lightning regions go beyond the traditional extrapolation-based forecast methods. In this work, we propose a data-driven neural network model for hourly lightning forecast, which exploits both the numerical simulations and the recent historical lightning observations. The two kinds of data complement each other and play different roles at different stages of the forecast. The use of dual-source data greatly increases the amount of information available to improve the forecasting performance. To handle the variability of deviation patterns in numerical simulations, we introduce a channel-wise attention mechanism, which adaptively adjusts the proportion of each simulated parameter to maximize the useful information. The attention mechanism also enables the model to reveal the contribution of each simulated parameter for the forecast. Experimental results on a real-world dataset show that the proposed method outperforms several baseline methods. Ablation studies further demonstrate the effectiveness of our data fusion approach and attention module. INDEX TERMS Deep learning, lightning forecast, spatiotemporal data mining, convolutional neural network, channel-wise attention.

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Section: Attention Mechanisms In Sequential Deep Learningmentioning

confidence: 99%

Attention-Based Dual-Source Spatiotemporal Neural Network for Lightning Forecast

Lin

Geng

et al. 2019

IEEE Access

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“…The demand forecasting using artificial neural networks (Slimani et al, 2015(Slimani et al, , 2017Bousqaoui et al, 2018;Chawla et al, 2019) and optimized by artificial bee colony (Sultan and Jasim, 2016). The recurrent neural networks for sales prediction (Chen et al, 2018). Likewise, the comparison of forecasts based on neural networks vs. statistical methods.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks in the Demand Forecasting of a Metal-Mechanical Industry

Lorente-Leyva¹,

Patiño-Alarcón²,

Montero-Santos³

et al. 2019

J. Eng. Appl. Sci.

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This research presents an application of artificial neural networks in demand forecasting by using MATLAB Software. Keeping in mind that in any planning process forecasts play a fundamental role, being one of the bases for; planning, organizing and controlling production. It gives priority to the most critical nodes and their key activities, so that, the decisions made about them will generate the greatest possible positive impact. The methodology applied demonstrates the quality of the solutions found which are compared with traditional statistical methods to demonstrate the value of the solution proposed. When the results show that the minimum quadratic error is reached with the application of artificial neural networks, a better performance is obtained. Therefore, a suitable horizon is established for the planification and decision making in the metal-mechanical industry for the use of artificial intelligence in the production processes.

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“…Thus, reasonable modelling of the influential factors and historical sales information should be performed to successfully predict sales volume. The research goal in this task is to: (1) review, test, and analyze state-of-the-art time series prediction models in terms of their efficacy in sales prediction; and (2) propose a new model to advance the performance in real-life sales prediction applications.…”

Section: Sales Predictionmentioning

confidence: 99%

“…Unlike conventional top-k recommendation, the sequential top-k recommendation approaches model the user behavior as a sequence of items instead of a set of items [22]. The research goal in this task is to: (1) review, test, and analyze the effectiveness of the latest sequential recommendation approaches; and (2) propose a novel sequential recommendation model that achieves state-of-the-art performance under the sequential top-k recommendation setting.…”

Section: Sequential Recommendationmentioning

confidence: 99%

“…In recent years, a large body of FM variants are proposed to better capture the effect of feature interactions, and are successfully applied to temporal predictive analytics. Hence, the research goal in this task is to: (1) review, test, and analyze existing FM-based models' adaptivity and effectiveness on temporal predictive analytics; and (2) propose a new, versatile, and high-performance FM-based model that can be generalized to various temporal ranking, classification, and regression tasks.…”

Section: Temporal Predictive Analyticsmentioning

confidence: 99%

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Sequence modelling for e-commerce

Chen¹

Self Cite

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With the proliferation of electronic commerce (e-commerce), the data generated by both customers and service providers can accumulate at a fast rate. As such, analyzing the rich but subtle patterns within the e-commerce data offers a prominent opportunity of refining user experience and increasing business revenue. Due to the high velocity of e-commerce data, sequence modelling plays a pivotal role in delivering timely predictive analytics and recommendations. Based on the granularity of data, sequence modelling for e-commerce is mainly conducted at two levels, namely macro-level modelling and micro-level modelling. When researching on e-commerce data, macro-level sequence modelling aims During my 3.5-year PhD candidature at The University of Queensland (UQ), I have come across a lot of wonderful people that offered me tremendous help, support, and advice. I would give my supervisors, Dr. Hongzhi Yin and Prof. Xue Li a big thanks for their continuous academic guidance, scholarship support, and patient advisory along my way of research. Dr. Hongzhi Yin has set me a great example of a young scholar with superb visions, capabilities and humbleness, and I have been so lucky to join his research team and contribute to the research goal that we believe will greatly pay back to the society. He is my constant reminder that I still have a lot to learn about what we are working on. Prof. Xue Li is the guide that opened the door of conducting research at UQ for me. Despite his seniority, I can always feel his endless energy and passion for research whenever we have a chat, while his kindness and insistence are the benchmarks I need to bear in mind throughout my lifetime. Apart from my advisory team, I would like to thank many other academic and professional staff at UQ and other institutes, especially A

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TADA: Trend Alignment with Dual-Attention Multi-task Recurrent Neural Networks for Sales Prediction

Cited by 69 publications

References 26 publications

Attention-Based Dual-Source Spatiotemporal Neural Network for Lightning Forecast

Attention-Based Dual-Source Spatiotemporal Neural Network for Lightning Forecast

Artificial Neural Networks in the Demand Forecasting of a Metal-Mechanical Industry

Sequence modelling for e-commerce

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