#Timenet ca series#
The representations or embeddings given by a pre-trained TimeNet are found to be useful for time series classification (TSC).
Once trained, TimeNet can be used as a generic off-the-shelf feature extractor for time series. Rather than relying on data from the problem domain, TimeNet attempts to generalize time series representation across domains by ingesting time series from several domains simultaneously. Inspired by the tremendous success of deep Convolutional Neural Networks as generic feature extractors for images, we propose TimeNet: a deep recurrent neural network (RNN) trained on diverse time series in an unsupervised manner using sequence to sequence (seq2seq) models to extract features from time series.
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