To address these challenges, this study develops a hybrid deep learning model that captures both local temporal dependencies and global contextual features. As illustrated in Figure 1, we propose a ...

Basic-Pytorch-LSTM-Program This is a very basic beginners version of a text prediction model, made in python using Pytorch. Essentially, you store the data in a text file (data.txt is hardcoded into ...

Abstract This study proposes a hybrid modeling approach that integrates a Physics Informed Neural Network (PINN) and a long short-term memory (LSTM) network to predict river water temperature in a ...

Seismic facies analysis, as a crucial step in the study of depositional facies, effectively delineates the distribution patterns of depositional facies between wells. To address the limitations of ...

While Long Short-Term Memory (LSTM) networks excel in handling time series data, Bayesian optimisation techniques offer significant advantages in tuning model parameters to adapt to variable matching ...

This code imports necessary libraries, preprocesses stock data, builds and trains an LSTM model for stock price prediction, and visualizes training and test results, including future value prediction.