Transcription of Time-Aware Multi-Scale RNNs for Time Series Modeling
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Time-Aware Multi-Scale RNNs for Time Series Modeling
Time-Aware Multi-Scale RNNs for time Series ModelingZipeng Chen1,Qianli Ma1;2 andZhenxi Lin11 School of Computer Science and Engineering,South China University of Technology, Guangzhou, China2 Key Laboratory of Big Data and Intelligent Robot(South China University of Technology), Ministry of information is crucial for modelingtime Series . Although most existing methods con-sider multiple scales in the time - Series data, theyassume all kinds of scales are equally importantfor each sample, making them unable to capturethe dynamic temporal patterns of time Series . Tothis end, we propose Time-Aware Multi-Scale Re-current Neural Networks (TAMS-RNNs), whichdisentangle representations of different scales andadaptively select the most important scale for eachsample at each time step. First, the hidden stateof the RNN is disentangled into multiple inde-pendently updated small hidden states, which usedifferent update frequencies to model time -seriesmulti-scale information.
model to capture the dynamic temporal patterns of time se-ries. We update the small hidden states independently to learn the representations of different scales better. Meanwhile, the temporal context information is used to select the most impor-tant scale at each time step adaptively, capturing more com-plicated temporal patterns.
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