Nonlinear regression can be used to model phenomena such as exponential growth, decay, saturation, oscillation, and logistic growth. Add your perspective Help others by sharing more (125 ...

Exponential regression is often used to model population growth, radioactive decay, or compound interest. Add your perspective. Help others by sharing more (125 characters min.) Cancel ...

Python package that analyses the given datasets and comes up with the best regression representation with either the smallest polynomial degree possible, to be the most reliable without overfitting or ...

I use Python 3 and Jupyter Notebooks to generate plots and equations with linear regression on Kaggle data. I checked the correlations and built a basic machine learning model with this dataset.

This paper focuses on predicting wind power using ten distinct models, including Gaussian Process Regression (GPR) with proper tunning of kernel function. Thereafter, the same wind power prediction is ...

To address this issue, Yuzhe Yang, Kaiwen Zha, Ying-Cong Chen, Dina Katabi from the Massachusetts Institute of Technology, and Hao Wang from the Rutgers University have introduced Deep Imbalanced ...

The Multivariate Linear Regression model achieves an impressive R-squared value of 0.9959, indicating high accuracy in predicting economic indicators. Furthermore, the Exponential Smoothing model ...

Crane, D. B., and James R. Crotty. "A Two-Stage Forecasting Model: Exponential Smoothing and Multiple Regression." Management Science 13, no. 8 (April 1966 ...

Python package that analyses the given datasets and comes up with the best regression representation with either the smallest polynomial degree possible, to be the most reliable without overfitting or ...