Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series

Understand

Leveraging the symmetries inherent to specific data domains for the construction of equivariant neural networks has lead to remarkable improvements in terms of data efficiency and generalization.

• However, most existing research focuses on symmetries arising from planar and volumetric data, leaving a crucial data source largely underexplored: time-series.
• In this work, we fill this gap by leveraging the symmetries inherent to time-series for the construction of equivariant neural network.
• We identify two core symmetries: *scale and translation*, and construct scale-translation equivariant neural networks for time-series learning.