Nov 15, 2021 · We propose a novel architecture for synthetically generating time-series data with the use of Variational Auto-Encoders (VAEs). The proposed architecture has several distinct properties: interpretability, ability to encode domain knowledge, and reduced training times.

Aug 14, 2024 · Variational autoencoders (VAEs) are a form of generative AI that came into the spotlight for their ability to create realistic images, but they can also create compelling time series. The standard VAE can be adapted to capture periodic and sequential patterns of time series data, and then be used to generate plausible simulations.

Sep 16, 2024 · Variational Autoencoders (VAEs) offer a robust solution to this problem by efficiently capturing the temporal dependencies and inherent structure in time-series data. In this post, we’ll...

TimeVAE is a model designed for generating synthetic time-series data using a Variational Autoencoder (VAE) architecture with interpretable components like level, trend, and seasonality. This repository includes the implementation of TimeVAE, as well as two baseline models: a dense VAE and a convolutional VAE.

Apr 10, 2024 · There are two types of autoencoders available: - AE (autoencoder) - VAE (variational autoencoder) The autoencoders can be easily parametrized using hyperparameters. ------------------------------------------------------------------------------------------- This code can also be considered as supplemental Material to the Paper:

Dec 9, 2024 · By leveraging VAEs, we can capture the underlying patterns of time series data in a compressed form. This can help us visualize the data more effectively, reduce dimensionality, and even...

Oct 15, 2024 · This study employs a data-driven approach to studying physical system vibrations, focusing on two main aspects: using variational autoencoders (VAEs) to generate physical data (i.e. data “similar” to those obtained via real-world processes) and using transformers in order to continuously forecast flexible body nonstationary vibrations (2D time-s...

In this paper, we generate synthetic training samples of time series data using a simple implementation of the Variational Autoencoder, to test whether classification performance increases when augmenting the original training sets with manifolds of generated samples.

Jan 28, 2022 · We propose a novel architecture for synthetically generating time-series data with the use of Variational Auto-Encoders (VAEs). The proposed architecture has several distinct properties: interpretability, ability to encode domain knowledge, and reduced training times.

To do the automatic time window isolation we need a time series anomaly detection machine learning model. The goal of this post is to introduce a probabilistic neural network (VAE) as a time series machine learning model and explore its use in the area of anomaly detection.