# Multiresolution Signal Processing of Financial Market Objects

arXiv: https://arxiv.org/abs/2210.15934 · HTML: https://mlsanity.com/paper/2210.15934/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Generating Diverse High-Fidelity Images with VQ-VAE-2](https://mlsanity.com/paper/1906.00446/) (2019)
- [Hierarchical Quantized Autoencoders](https://mlsanity.com/paper/2002.08111/) (2020)
- [Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio](https://mlsanity.com/paper/2002.02008/) (2020)

## Built on by (most cited first, 1 of 1)

- [Yield Curves Dynamics Using Variational Autoencoders Under No-arbitrage](https://mlsanity.com/paper/2605.12764/) (2026)

## Abstract (arXiv)

Multiresolution analysis has applications across many disciplines in the study of complex systems and their dynamics. Financial markets are among the most complex entities in our environment, yet mainstream quantitative models operate at predetermined scale, rely on linear correlation measures, and struggle to recognize non-linear or causal structures. In this paper, we combine neural networks known to capture non-linear associations with a multiscale decomposition to facilitate a better understanding of financial market data substructures. Quantization keeps our decompositions calibrated to market at every scale. We illustrate our approach in the context of seven use cases.