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.

Aug 14, 2024 · Variational autoencoders reduce the dimensions of the input data into a smaller subspace. VAEs define an encoder to transform observed inputs into a compressed form called the latent variable. Then, a distinct, mirroring decoder attempts to recreate the original data.

Sep 16, 2024 · By leveraging sequential architectures, VAEs can compress large volumes of time-series data into a compact latent space, making it easier to store, analyze, and detect anomalies in real-time.

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.

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...

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.

Oct 1, 2024 · Unique Augmentation Algorithm: Combines variational autoencoders with metric learning for time series. Normalization of Irregularities: Normalizes heteroscedastic and non-stationary data effectively. Advanced VAE Integration: Integrates GRU into VAE, enhanced by metric learning for better representation.

I am working on a Variational Autoencoder (VAE) to detect anomalies in time series. So far I worked with this tut https://blog.keras.io/building-autoencoders-in-keras.html and this https://wiseodd.github.io/techblog/2016/12/10/variational-autoencoder/.

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.

Therefore, we present a novel variational autoencoder approach to generate time series data on a probabilistic latent feature representation and enhance interpretability within the generative model and the output trajectory.