Heteroscedasticity describes a situation where risk (variance) changes with the level of a variable. In financial models, this means volatility is not constant. Most pricing and forecasting models ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Harvard University is providing seven free online courses in data science, each running for eight to nine weeks and requiring ...

The line between human and artificial intelligence is growing ever more blurry. Since 2021, AI has deciphered ancient texts ...

Abstract: In modern era, time series forecasting plays a crucial role in Solar Power Generation (SPG), by allowing the prediction of future energy output using past power and weather data for grid ...

. ├── main.py # Streamlit application ├── src/ │ ├── models/ │ │ ├── rough_set_fuzzy.py # Rough set fuzzy logic model │ │ └── intuitionistic_fuzzy.py # Intuitionistic fuzzy logic model │ └── utils/ │ ...

1 Faculty of Electrical Technology and Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia. 2 Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia ...

ABSTRACT: Accurate forecasting of the system marginal price (SMP) is crucial to improve demand-side management and optimize power generation scheduling. However, predicting the SMP is challenging due ...

Temperature impacts every part of the world. Meteorological analysis and weather forecasting play a crucial role in sustainable development by helping reduce the damage caused by extreme weather ...

Introduction: Transformer models have demonstrated remarkable performance in financial time series forecasting. However, they suffer from inefficiencies in computational efficiency, high operational ...