Beyond the lakehouse: Fundamental's NEXUS bypasses manual ETL with a native foundation model for tabular data

Emerging from stealth, the company is debuting NEXUS, a Large Tabular Model (LTM) designed to treat business data not as a simple sequence of words, but as a complex web of non-linear relationships.
GitHub

LSTM-based Li-ion Battery Aging Prediction

PyTorch Lightning implementation of an LSTM-based encoder-decoder for battery aging prediction. The model is optimized for training/prediction speed and prediction accuracy and can be applied for ...
C&EN

Research on Remaining Useful Life Prediction of PEMWE Stack Based on the LSTM-iTransformer Model

In the context of global energy shortages, traditional energy sources face issues of limited reserves and high prices. As a result, the importance of energy storage technology is increasingly ...
devdiscourse

AI-powered forecasting boosts retail efficiency: LSTM outshines traditional models

In a new comparative analysis of artificial intelligence applications in retail, researchers have revealed that advanced deep learning models can dramatically enhance the accuracy of demand ...
GitHub

Stock Price Prediction Model

The goal of this project is to predict future stock prices by training on historical stock data. Please describe the specific objective here, such as: [e.g., to predict the closing price for a ...
Frontiers

A high-precision displacement prediction model for landslide geological hazards based on APSO-SVR-LSTM combination

The development of a high-precision displacement prediction model for landslide geological hazards is crucial for the early warning of such disasters. Landslide deformation typically exhibits a ...
Frontiers

Forecasting sea level rise using enhanced deep learning models

College of Engineering and IT, University of Dubai, Dubai, United Arab Emirates Climate change has significantly impacted vulnerable communities globally, with rising temperatures caused by greenhouse ...
IEEE

A Variational Inference-Based LSTM-Enhanced Deep Neural Model for Sequential Recommendations

Abstract: Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static ...
Scientific Research Publishing

Kim, H., & Won, C. (2018). Forecasting the Volatility of Stock Price Index: A Hybrid Model Integrating LSTM with Multiple GARCH-Type Models. Computational Economics, 51, 29-49.

ABSTRACT: The application of artificial intelligence in stock price forecasting is an important area of research at the intersection of finance and computer science, with machine learning techniques ...
Scientific Research Publishing

Kulshreshtha, S., & Vijayalakshmi, A. (2020). An ARIMA-LSTM Hybrid Model for Stock Market Prediction Using Live Data. Journal of Engineering Science and Technology Review, 13 ...

ABSTRACT: The application of artificial intelligence in stock price forecasting is an important area of research at the intersection of finance and computer science, with machine learning techniques ...