Optimizing stock price forecasting: a hybrid approach using fuzziness and automated machine learning

Time series forecasting, particularly in the domain of stock prices, is a significant challenge but benefits from the availability of openly accessible data. Our work focuses mainly on (although not limited to) univariate time series forecasting of monthly or daily stock prices, predicting one step ahead. We developed an innovative pipeline that combines fuzzification with Automated Machine Learning, achieving improved forecasting performance. Unlike previous literature, we revise the binomial Fuzzy Time Series and machine learning algorithm, including a classification task (formally motivating it), and involving unused features of fuzzy sets. Thanks to the type of aggregation of the fuzzified data, the approach has the potential to preserve interpretability, unlike most machine learning based approaches. Using several financial datasets, in addition to preliminary experiments on chaotic time series, we found evidence of significantly improved performance in most cases. This study contributes to further understanding of the intersection between fuzzy logic and Automated Machine Learning, particularly in the context of time series forecasting, offering a promising direction for future research.