AdaRNN
AdaRNN
**AdaRNN** is an adaptive [RNN](https://paperswithcode.com/methods/category/recurrent-neural-networks) that learns an adaptive model through two modules: [Temporal Distribution Characterization](https://paperswithcode.com/method/temporal-distribution-characterization) (TDC) and [Temporal Distribution Matching](https://paperswithcode.com/method/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](https://paperswithcode.com/methods/category/recurrent-neural-networks)-based model.
AdaRNN: Adaptive Learning and Forecasting of Time Series
https://paperswithcode.com/paper/adarnn-adaptive-learning-and-forecasting-of
AdaRNN: Adaptive Learning and Forecasting of Time Series