Objective: Perform nonlinear and multivariate regression on energy data to predict oil price. Predictors are data features that are inputs to calculate a predicted output. In machine learning the data inputs are called features and the measured outputs are called labels. Regression is the method ...
Our Python package is unique in that it is the only package we know that is completely free to use that focuses exclusively on multivariate synchrony, and the only one to offer our particular combination of metrics, the value of which is demonstrated by the fact that these metrics have been ...
LsNGC is formulated as a multiple regression problem with nonlinear basis transformation using GRBF with \((T - (d-1))\) samples in the regression task. Evidently, the larger the number of samples, higher the power of the hypothesis tests. An advantage of lsNGC’s formulation is that it...
In recent years, more and more studies attempted to apply Machine Learning (ML) models to forecast incidence, such as Nonlinear Autoregressive (NAR) neural network, General Regression Neural Network (GRNN), Support Vector Machine (SVM) [14]. Many studies have proved that the accuracy of ML ...
we use theprocess_time_nsfunction from standard Python library (Stinner2017) which has the highest temporal resolution with 1 nano second. To compensate the inaccuracies due to the parallel occurring load or further inaccuracies in the determination of the runtime, all experiments for the analysis...
And in terms of software, our experiments rely on the tensorflow 2.7.0 framework, programming with Python 3.8.12, the GPU driver is CUDA 11.2, and the operating system is Windows 11. The sliding window size of the experimental data is uniformly set to Experimental results of the Lorenz ...
Library for multivariate function approximation with splines (B-spline, P-spline, and more) with interfaces to C++, C, Python and MATLAB Topics python c-plus-plus interpolation smoothing splines function-approximation b-splines p-spline Resources Readme License MPL-2.0 license Activity Stars...
This package constructs State-Space models that can include highly flexible nonlinear predictor effects for both process and observation components by leveraging functionalities from the impressive brms and mgcv packages. This allows mvgam to fit a wide range of models, including hierarchical ecological ...
A feature extraction algorithm was implemented using Python 3.7 and scientific data libraries Scikit-image, Scipy and Numpy, Pandas, and Scikit-learn were used for data processing and analysis. Matplotlib and Seaborn libraries were used for plotting. For feature extraction, statistical analysis of ...
The experiment was conducted using PyTorch version 1.13.1 in Python 3.9.16, with training performed on a GeForce RTX Titan GPU with 48 GB of RAM. The NetCDF and NumPy libraries were employed for data input and storage. The model utilized Soft-DTW as the loss function and Adam as the opt...