AdaRNN

2000년 도입 · 논문 1편에서 사용

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.

출처: AdaRNN: Adaptive Learning and Forecasting of Time Series

소개 논문: AdaRNN: Adaptive Learning and Forecasting of Time Series