MSTAR: Multi-Scale Backbone Architecture Search for Timeseries Classification

arXiv:2402.13822v1 Announce Type: new
Abstract: Most of the previous approaches to Time Series Classification (TSC) highlight the significance of receptive fields and frequencies while overlooking the time resolution. Hence, unavoidably suffered from scalability issues as they integrated an extensive range of receptive fields into classification models. Other methods, while having a better adaptation for large datasets, require manual design and yet not being able to reach the optimal architecture due to the uniqueness of each dataset. We overcome these challenges …

abstract architecture arxiv classification cs.cv fields highlight scalability scale search series significance time series timeseries type