Citekey | WangEtAl2019Study |
Source code | own |
Learning type | semi-supervised |
Input dimensionality | univariate |
- python 3
- pytorch
The model uses wavelet basis functions (WBF) as activation functions for the hidden layer. The following plot shows the 3 different options:
The model keeps an internal threshold, which is the upper percentile of the normal distribution of its training errors. When the model detects a point whose error exceeds this threshold, the next sliding window changes the outlier point with the predicted point.
OceanWNN forecasts points based on a preceding window. Hence, the first window_size
points do not have a score.
Using LSTMs instead of Linear layers to allow multivariate data.