In this paper, we present an incremental local distribution-based clustering algorithm with the Bayesian adaptive resonance theory (ILBART). This algorithm is developed to adapt itself to a changing ...

Ignores randomness; every forecast will have an element of unpredictability ... markets change – refining forecasts based on new data, rather than using fixed assumptions. For example: Before COVID, a ...

We propose a Bayesian model averaging (BMA) post-processing method suitable for forecasting power from utility-scale photovoltaic (PV) plants at multiple time horizons up to at least the day-ahead ...

Author Ankur Patel provides practical knowledge on how to apply unsupervised learning using two simple, production-ready Python frameworks - scikit-learn and TensorFlow. With the hands-on examples and ...

Python for cybersecurity with the basic concepts, easy to understand code examples, lab exercises, real-world examples, different security scripts covering web security, network security, defensive ...