An Explainable Estimation Model for Financial Time Series

Citation

Takahashi O, Cuyt A, O'Hare A & Lee W (2026) An Explainable Estimation Model for Financial Time Series. In: 21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems Short Paper Proceedings. The 21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2026), Sapienza University of Rome, Rome, Italy, 15.06.2026-19.06.2026, pp. 241-245.

Abstract
Recent developments in artificial intelligence have led to powerful machine learning approaches to model fluctuating dynamics in financial time series, even in the presence of non-stationary behavior. However, such methods often suffer from limited interpretability while uncertainty, arising from complex data fluctuations, remains difficult to quantify. We present a method that decomposes financial data into different frequency components and trends, thereby enabling explicit identification of cyclical patterns and long-term trends. Concurrently, noise components are separated from signal components. Its effectiveness and its high predictive accuracy have been demonstrated in the interpretation of some financial time series.