Deep Learning for time Series Classification - a Review
- Convolution Neural Network
- Applying several filters on a time series will result in a multivariate time series whose dimensions are equal to the number of filters used.
- An intuition behind applying several filters on an input time series would be to learn multiple discriminative features useful for the classification task.
- Applying several filters on a time series will result in a multivariate time series whose dimensions are equal to the number of filters used.
Recurrent Neural Network(Recurrent Neural Network) 은 다음과 같은 이유로 TSC 에 잘 사용되지 않는다.
- (1) the type of this architecture is designed mainly to predict an output for each element (time stamp) in the time series.
- (2) RNNs typically suffer from the vanishing gradients problem due to training on long
time series (Pascanu et al., 2012);
- (3) RNNs are considered hard to train and parallelize which led the researchers to avoid using them for computational reasons.