signal-decomposition

PyAF is an Open Source Python library for Automatic Time Series Forecasting built on top of popular pydata modules.

A Python library for signal decomposition algorithms.

Python codes “Jupyter notebooks” for the paper entitled "A Hybrid Method for Condition Monitoring and Fault Diagnosis of Rolling Bearings With Low System Delay, IEEE Trans. on Instrumentation and Measurement, Aug. 2022. Techniques used: Wavelet Packet Transform (WPT) & Fast Fourier Transform (FFT). Application: vibration-based fault diagnosis.

A comprehensive Python library for extracting statistical features from 1D signals using advanced decomposition techniques and signal processing metrics.

2DS-L: A dynamical system decomposition of signal approach to learning with application in time series prediction. This research proposes a novel approach for time series forecasting using dynamical systems, signal processing, and Neural Networks, which wit named 2DS-L.

Variational Decomposition Autoencoders - Codebase repository for paper: "Variational decomposition autoencoding improves disentanglement of latent representations"

Enhancing time series forecasting stability with a connected neural dynamical system approach

Simulations I have created and interesting interpolations of data I have done. Projects include: Simple gaussian decomposition which I did to absorbance spectra from my physical chemistry course as an addition to the normal course work; a simple molecular dynamics simulation; and possibly coming soon a molecular simulation/ coupled cluster method.

Leak-free causal deep learning for day-ahead short-term load forecasting (PyTorch + MATLAB). Adaptive dual-stage decomposition + Patch Transformer-BiGRU with cross-attention; honest 5-seed Diebold-Mariano protocol.