The study introduces a novel hybrid Variational Autoencoder-SURF (VAE-SURF) model for anomaly detection in crowded environments, addressing critical challenges such as scale variance and temporal ...

Though long-term and seasonal trends are vital in time series foreasting, it may be insufficient for those not having such obvious characters. This paper proposes a time series forecasting model named ...

We propose VisionTS, a time series forecasting (TSF) foundation model building from rich, high-quality natural images 🖼️. We reformulate the TSF task as an image reconstruction task, which is further ...

Time Series Forecasting Based on Structured Decomposition and Variational Autoencoder Abstract: Time series forecasting based on decomposition method usually decomposes a complex time series into some ...

Deep learning methods for generating artificial data in health care include data augmentation by variational autoencoders (VAE) technology. Objective: We aimed to test the feasibility of generating ...

In this article, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder (VAE)-based techniques. These techniques require a small number of ...

Keywords: feature extraction, variational autoencoder, ECG, electrocardiography, deep learning, explainable AI Citation: Kuznetsov VV, Moskalenko VA, Gribanov DV and Zolotykh NY (2021) Interpretable ...

Auto encoder for time series. Contribute to RobRomijnders/AE_ts development by creating an account on GitHub.