AdaRNN is an adaptive RNN that learns an adaptive model through two modules: Temporal Distribution Characterization (TDC) and Temporal Distribution Matching (TDM) algorithms. Firstly, to better characterize the distribution information in time-series, TDC splits the training data into $K$ most diverse periods that have a large distribution gap inspired by the principle of maximum entropy. After that, a temporal distribution matching (TDM) algorithm is used to dynamically reduce distribution divergence using a RNN-based model.
Source: AdaRNN: Adaptive Learning and Forecasting of Time SeriesPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Activity Recognition | 1 | 33.33% |
Human Activity Recognition | 1 | 33.33% |
Time Series Analysis | 1 | 33.33% |
Component | Type |
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Temporal Distribution Characterization
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Time Series Modules | |
Temporal Distribution Matching
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Time Series Modules |