Recurrent Neural Networks
AdaRNN
Introduced by Du et al. in AdaRNN: Adaptive Learning and Forecasting of Time SeriesAdaRNN 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 Series
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Activity Recognition | 1 | 33.33% |
| Human Activity Recognition | 1 | 33.33% |
| Time Series Analysis | 1 | 33.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
Temporal Distribution Characterization
|
Time Series Modules | |
Temporal Distribution Matching
|
Time Series Modules |
