IEEE

Tutorial on Empirical Mode Decomposition: Basis Decomposition and Frequency Adaptive Graduation in Non-Stationary Time Series

Abstract: This tutorial explores the class of non-parametric time series basis decomposition methods particularly suited for nonstationary time series known as Empirical Mode Decomposition (EMD). In ...
Hacker

A Quick Introduction to Python Numpy for Beginners

I like Anime, Chess, Deep Learning, Mathematics and Programming. NumPy is a Python library that is mainly used to work with arrays. An array is a collection of items that are stored next to each other ...
GitHub

tsod: Anomaly Detection for time series data.

Sensors often provide faulty or missing observations. These anomalies must be detected automatically and replaced with more feasible values before feeding the data to numerical simulation engines as ...
IEEE

Bayesian Neural Networks via MCMC: A Python-Based Tutorial

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...
GitHub

Pyvisco is a Python library that supports the identification of Prony series parameters for Generalized Maxwell models describing linear viscoelastic materials.

The mechanical response of linear viscoelastic materials is often described with Generalized Maxwell models. The necessary material model parameters are typically identified by fitting a Prony series ...