# Fast Training Algorithms for Deep Convolutional Fuzzy Systems with Application to Stock Index Prediction

arXiv: https://arxiv.org/abs/1812.11226 · HTML: https://mlsanity.com/paper/1812.11226/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Hierarchical fuzzy neural networks with privacy preservation for heterogeneous big data](https://mlsanity.com/paper/2209.08467/) (2022)
- [DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System](https://mlsanity.com/paper/2308.06378/) (2023)
- [Stock Market Analysis with Text Data: A Review](https://mlsanity.com/paper/2106.12985/) (2021)
- [A integrating critic-waspas group decision making method under interval-valued q-rung orthogonal fuzzy enviroment](https://mlsanity.com/paper/2201.01027/) (2022)

## Abstract (arXiv)

A deep convolutional fuzzy system (DCFS) on a high-dimensional input space is a multi-layer connection of many low-dimensional fuzzy systems, where the input variables to the low-dimensional fuzzy systems are selected through a moving window across the input spaces of the layers. To design the DCFS based on input-output data pairs, we propose a bottom-up layer-by-layer scheme. Specifically, by viewing each of the first-layer fuzzy systems as a weak estimator of the output based only on a very small portion of the input variables, we design these fuzzy systems using the WM Method. After the first-layer fuzzy systems are designed, we pass the data through the first layer to form a new data set and design the second-layer fuzzy systems based on this new data set in the same way as designing the first-layer fuzzy systems. Repeating this process layer-by-layer we design the whole DCFS. We also propose a DCFS with parameter sharing to save memory and computation. We apply the DCFS models to predict a synthetic chaotic plus random time-series and the real Hang Seng Index of the Hong Kong stock market.