Fast Training Algorithms for Deep Convolutional Fuzzy Systems with Application to Stock Index Prediction
This work addresses efficient training for fuzzy systems in time-series prediction, particularly for financial applications, but appears incremental as it builds on existing fuzzy methods.
The authors tackled the design of deep convolutional fuzzy systems (DCFS) for high-dimensional inputs by proposing a bottom-up layer-by-layer training algorithm, applying it to predict a synthetic chaotic time-series and the real Hang Seng Index stock market data.
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.