To the best of my knowledge, there are no existing code libraries that directly implement neural network quantile regression. Implementing neural quantile regression from scratch using C# allows you ...

The goal of a machine learning regression problem is to predict a single numeric value. Quantile regression is a variation where you are concerned with under-prediction or over-prediction. I'll phrase ...

It is a pleasure to introduce the latest issue of The Journal of Computational Finance. The first two contributions focus on using novel neural network machinery to enhance classical financial ...

Their study is centred around answering three research questions: Do ANNs perform better than the traditional multiple regression models in the prediction of lighting parameters and energy demand of ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

Researchers use a machine learning (ML) approach to obtain the EM-aware aging prediction of the power grid (PG) network. They use neural network–based regression as their core ML technique to ...

The oil spill is the main marine disaster. It is known that the data mining based method performs better in detecting oil than the traditional SAR based method to distinguish from lookalikes. Recently ...

With the onset of decentralized finance, the wave of blockchain innovations has also gotten a spark, as many projects are ...