Extending Machine Learning Prediction Capabilities by Explainable Ai in Financial Time Series Prediction

Prediction with higher accuracy is vital for stock market prediction. Recently, considerable amount of machine learning techniques are proposed which successfully predict stock market price direction. No matter how successful the proposed prediction model, it can be argued that there occur two major drawbacks for further increasing the prediction accuracy. The first one is that, because machine learning methods bear black box nature, the source of inference for the predictions cannot be explained. Furthermore, due to the complex characteristics of the predicted time series, no matter how sophisticated techniques are employed, it would be very difficult to achieve a marginal increase in accuracy that would meaningfully offset the additional computational burden it brings in. For these two reasons, instead of chasing incremental accuracy increases, we propose utilizing an eXplainable Artificial Intelligence (XAI) approach which can be employed for assessing the reliability of the predictions hence allowing decision maker to abstain from poor decisions which are responsible for decrease in overall prediction performance. If there would be a measure of how sure the prediction model is on any prediction, the predictions with a relatively higher reliability could be used to make a decision while lower quality decisions could be avoided. In this study, a novel two-stage stacking ensemble model for stock market direction prediction based on machine learning (ML), empirical mode decomposition (EMD) and XAI is proposed. Our experiments have shown that, proposed prediction model supported with local interpretable model-agnostic explanations (LIME) achieved the highest accuracy of 0.9913 with trusted predictions on the KOSPI dataset.