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Even if a time series is constructed up of numbers only, finding abstract fixed-dim vector representation would be interesting for classification/clustering purposes. As we can learn & find abstract representations/embeddings of text/images, can we do something similar on Time series? Finding such ways would result in better clustering & related tasks instead of traditional ways using some statistical measures like Pearson correlation etc. All thoughts are welcome.

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  • $\begingroup$ Have you heard of time-series segmentation algorithms? These seem to be what you are looking for. $\endgroup$ – Edmund Apr 18 '19 at 2:31
  • $\begingroup$ **Thanks @Edmund **, I hadn't heard that before; but now I have learned about it.This is similar to what I am looking for, but still not exact match. A survey on TS segmentation $\endgroup$ – awakened_iota Apr 18 '19 at 6:44
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Maybe the framework of Neural Processes could be interesting here? It defines a family of functions parameterized by a neural network. The parameters could serve as your embeddings, eventually after projecting into a lower dimension. See the paper Attentive Neural Processes and the preceding papers cited therein.

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