In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent...

A regularization approach to model selection, within a generalized HJM framework, is introduced, which learns the closest arbitrage-free model to a prespecified factor model. This optimization problem is represented as the limit of a one-parameter family of computationally tractable penalized...

Due to its randomness, intermittence, and volatility, the high-proportional integration of wind and solar power poses challenges to the safe and stable operation of power systems. Cascade hydropower stations have a high response speed, high adjustability, and stable output. This study proposed a...

While individual investors may have more diverse preferences and trading behavior than institutional investors due to their lack of professional education, many studies tend to lump individual investors together or classify them by socio-demographic characteristics. We conducted an empirical...

This study focuses on developing and evaluating predictive models for bus travel times adaptable to any transit network, or to new roadway segments without prior travel time data. Most prior work relies on non-standardized features such as road traffic forecasts or closed-source datasets to test...