Forecasting financial time series can be challenging because of the inherent complex
interplay of temporal and spatial dynamics in the data. Specifically, time series for different …

This paper presents a comprehensive framework for expert-augmented supervised feature
selection, addressing pre-processing, in-processing, and post-processing aspects of …

The present study proposes a multi-criteria framework that focuses on two conflicting
objectives typically encountered in building energy management systems: energy …

This note focuses on the optimization of neural architectures for stock index movement
forecasting following the onset of a major market disruption or crisis. Given that such crises …

Investment strategies in the stock market are intricate, hinging upon the analysis of extensive
datasets. Recently, there has been a growing interest in leveraging machine learning …

Deep learning forecasting models that rely on historical data struggle in financial markets
because the historical data provide insufficient information for adapting to new market …