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.

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.

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.

Mar 13, 2023 · Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables. Recent studies show that VAE can flexibly learn the complex temporal dynamics of time series and achieve more promising forecasting results than deterministic models.

Dec 9, 2024 · Variational Autoencoders (VAEs) have emerged as a powerful tool in machine learning, particularly for generating new data from learned representations. In this post, we will explore a practical...

Jul 3, 2019 · In view of reconstruct ability of the model and the calculation of anomaly score, this paper proposes a time series anomaly detection method based on Variational AutoEncoder model (VAE) with re-Encoder and Latent Constraint network (VELC).

Dec 21, 2020 · In this post, we introduced an application of Variational AutoEncoder for time-series analysis. We built a VAE based on LSTM cells that combines the raw signals with external categorical information and found that it can effectively impute missing intervals.

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...

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.