Methods › Sequential › Recurrent Neural Networks › AdaRNN
AdaRNN
Introduced by Yuntao Du et al. in AdaRNN: Adaptive Learning and Forecasting of Time Series
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
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.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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AdaRNN: Adaptive Learning and Forecasting of Time Series 10 Aug 2021 · 2 repositories · arXiv:2108.04443
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Activity Recognition | 1 |
| Financial Analysis | 1 |
| Human Activity Recognition | 1 |
| Time Series | 1 |
| Time Series Analysis | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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