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An Attention-Based Forecasting Network for Intelligent Services in Manufacturing by Xinyi Zhou; Xiaofeng Gao is a book available to read on EtoBox.

What is An Attention-Based Forecasting Network for Intelligent Services in Manufacturing about?

Multivariate temporal data generally exists in the whole manufacturing process and forecasting lays the foundation for many intelligent services in industry. In this paper, we propose an end-to-end deep learning framework named dual-dimensional attention-based network (DANet) to solve the multivariate time series forecasting problem in industry. It leverages the strengths of recurrent neural network (RNN) structures to discover the underlying temporal patterns of the multidimensional input. A recurrent module is used for capturing sequential relationships between adjacent timesteps and embedding the original observations. Then, we apply a novel dual-dimensional attention mechanism to cope with the intrinsic characteristics of industrial big data. Feature-wise self-attention enables the network to adaptively learn the correlations between features, while time-wise attention captures complex long and short-term temporal dependencies. Our model shows its advantages over the baseline methods and a more stable and robust performance in the experiments on several real-world manufacturing datasets.

Author

Xinyi Zhou; Xiaofeng Gao

Publisher

Springer International Publishing : Imprint: Springer

Published

2021

Language

EN

ISBN

9783030914318

Subjects

Computer Science, Business, Technology