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Inspired by the tremendous success of deep Convolutional Neural Networks asgeneric feature extractors for images, we propose TimeNet: a deep recurrentneural network (RNN) trained on diverse time series in an unsupervised mannerusing sequence to sequence (seq2seq) models to extract features from timeseries. Rather than relying on data from the problem domain, TimeNet attemptsto generalize time series representation across domains by ingesting timeseries from several domains simultaneously. Once trained, TimeNet can be usedas a generic off-the-shelf feature extractor for time series. Therepresentations or embeddings given by a pre-trained TimeNet are found to beuseful for time series classification (TSC). For several publicly availabledatasets from UCR TSC Archive and an industrial telematics sensor data fromvehicles, we observe that a classifier learned over the TimeNet embeddingsyields significantly better performance compared to (i) a classifier learnedover the embeddings given by a domain-specific RNN, as well as (ii) a nearestneighbor classifier based on Dynamic Time Warping.

TimeNet: Pre-trained deep recurrent neural network for time series classification