


Over the embeddings given by a domain-specific RNN, as well as (ii) a nearest Yields significantly better performance compared to (i) a classifier learned Vehicles, we observe that a classifier learned over the TimeNet embeddings

For several publicly availableĭatasets from UCR TSC Archive and an industrial telematics sensor data from Useful for time series classification (TSC). Representations or embeddings given by a pre-trained TimeNet are found to be Once trained, TimeNet can be usedĪs a generic off-the-shelf feature extractor for time series. Series from several domains simultaneously. To generalize time series representation across domains by ingesting time Rather than relying on data from the problem domain, TimeNet attempts Using sequence to sequence (seq2seq) models to extract features from time Neural network (RNN) trained on diverse time series in an unsupervised manner Generic feature extractors for images, we propose TimeNet: a deep recurrent
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TIMENET ASSISTENZA MANUALS
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