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Latent Space Forecasting: An Integrated VAE-LSTM Framework for End-to-End Sequential Reconstruction Cover

Latent Space Forecasting: An Integrated VAE-LSTM Framework for End-to-End Sequential Reconstruction

Latvian Journal of Physics and Technical Sciences
Volume 63 (2026): Issue 2 (April 2026)
By: R. Rajeev  
Open Access
|Apr 2026

Authors

R. Rajeev

rajeev.ralla@edu.rtu.lv

Institute of Mechanical and Biomedical Engineering, Riga Technical University, Riga, Latvia
DOI: https://doi.org/10.2478/lpts-2026-0012 | Journal eISSN: 2255-8896 | Journal ISSN: 0868-8257
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Language: English
Page range: 57 - 73
Published on: Apr 1, 2026
Published by: Institute of Physical Energetics
In partnership with: Paradigm Publishing Services
Publication frequency: 6 issues per year
Keywords:
Hybrid deep-learning framework,
long short-term memory network,
variational autoencoder
Related subjects:
Physics,
Technical and applied physics

© 2026 R. Rajeev, published by Institute of Physical Energetics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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