A Neuro-Fuzzy System for Interpretable Long-Term Stock Market Forecasting
This addresses the problem of interpretable long-term forecasting for stock market analysts, but it appears incremental as it combines existing methods like LSTM and fuzzy systems without a major breakthrough.
The paper tackled the challenge of achieving both accuracy and interpretability in multivariate time series forecasting for stock markets, introducing the Fuzzy Transformer (Fuzzformer) which showed comparable performance to ARIMA and LSTM models on S&P500 data.
In the complex landscape of multivariate time series forecasting, achieving both accuracy and interpretability remains a significant challenge. This paper introduces the Fuzzy Transformer (Fuzzformer), a novel recurrent neural network architecture combined with multi-head self-attention and fuzzy inference systems to analyze multivariate stock market data and conduct long-term time series forecasting. The method leverages LSTM networks and temporal attention to condense multivariate data into interpretable features suitable for fuzzy inference systems. The resulting architecture offers comparable forecasting performance to conventional models such as ARIMA and LSTM while providing meaningful information flow within the network. The method was examined on the real world stock market index S\&P500. Initial results show potential for interpretable forecasting and identify current performance tradeoffs, suggesting practical application in understanding and forecasting stock market behavior.