| Abstrakt: |
Time series averages are one key input to temporal data mining techniques such as classification, clustering, forecasting, etc. In practice, the optimality of estimated averages often impacts the performance of such temporal data mining techniques. Practically, an estimated average is presumed to be optimal if it minimizes the discrepancy between itself and members of an averaged set while preserving descriptive shapes. However, estimating an average under such constraints is often not trivial due to temporal shifts. To this end, all pioneering averaging techniques propose to align averaged series before estimating an average. Practically, the alignment gets performed to transform the averaged series, such that, after the transformation, they get registered to their arithmetic mean. However, in practice, most proposed alignment techniques often introduce additional challenges. For instance, Dynamic Time Warping (DTW)-based alignment techniques make the average estimation process non-smooth, non-convex, and computationally demanding. With such observation in mind, we approach time series averaging as a generative problem. Thus, we propose to mimic the effects of temporal alignment in the latent space of multi-tasking neural networks. We also propose to estimate (augment) time domain averages from the latent space representations. With this approach, we provide state-of-the-art latent space registration. Moreover, we provide time domain estimations that are better than the estimates generated by some pioneering averaging techniques. [ABSTRACT FROM AUTHOR] |
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