Scientific Research Publishing

Gold Price Prediction Based on LSTM-Attention Combined Model ()

This paper explores effective methods for predicting gold prices, proposing three modeling strategies: a standalone Long Short-Term Memory (LSTM) network, a Convolutional Self-Attention (CSA) Network, ...
Frontiers

A hybrid LSTM–transformer model for accurate remaining useful life prediction of lithium ...

With the widespread application of lithium-ion batteries in electric vehicles and energy storage systems, health monitoring and remaining useful life prediction have become critical components of ...
Frontiers

A hybrid VMD-LSTM-SVR model for landslide prediction

Landslides are one of the most prevalent natural geological disasters, causing significant economic losses, damaging public environments, and posing severe threats to human lives. Landslide ...
Scientific Research Publishing

Hybrid PINN-LSTM Model for River Temperature Prediction: A Physics-Informed Deep Learning ...

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 defined ...
IEEE

LSTM-BP Model: Enhancing Prediction Accuracy for Medium Earth Orbit Satellites

Abstract: To improve the low accuracy of the SGP4 model in short-term orbit prediction for medium Earth orbit satellites and the instability in LSTM model training, this paper proposes and develops an ...
IEEE

A Sensitive LSTM Model for High Accuracy Zero-Inflated Time-Series Prediction

Abstract: The prevalence of zero values in zero-inflated time-series (ZI-TS) data poses significant challenges for traditional LSTM networks in learning long-term dependencies and trends. Specifically ...
GitHub

Time-Series-Forecasting-for-Portfolio-Management-Optimization

Task 1: Preprocess and Explore the Data 1.1 Load Historical Data (TSLA, BND, SPY) Using yfinance python CopyEdit import yfinance as yf import pandas as pd import numpy as np import matplotlib.pyplot ...