A deformable convolutional time-series prediction network with extreme peak and interval calibration

Xin Bi1,2, Guoliang Zhang1,2, Lijun Lu1,2, George Y Yuan3, Xiangguo Zhao4, Yongjiao Sun5, Yuliang Ma6
1Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Shenyang, China
2Key Laboratory of Liaoning Province on Deep Engineering and Intelligent Technology, Shenyang, China
3Thinvent Digital Technology Co. LTD, Nanchang, China
4College of Software, Shenyang, China
5School of Computer Science and Engineering, Shenyang, China
6College of Information Science and Engineering, Shenyang, China

Tóm tắt

Deep modeling and analysis of human big data deepens our understanding of human activities. Periodic time-series signals, e.g., electrocardiographs, collected by health monitoring sensors reflect human health status and assist in disease diagnosis. However, long-term prediction of these signals using deep learning models poses three challenges, namely, sparse features, conservative prediction of extreme peaks, and varying periodic intervals. We address these issues by proposing a prediction framework called EPIC with extreme peak and interval calibrations. EPIC consists of a triple-channel prediction network and a calibration network. The prediction network learns the time-domain, frequency-domain, and deformable features of time-series patterns simultaneously. Amplitude residuals of extreme peaks are emphasized in the designed training loss function. In addition, to alleviate the problem of unaligned predictions resulting from inaccurate periodic intervals, we further design a calibration module to reduce the deviation of periodic intervals. The experimental results and ablation studies indicate that EPIC achieves excellent performance in long-term prediction tasks.

Tài liệu tham khảo

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