Daily Excelsior

Machine Learning Methods Used for Portfolio Optimization and Risk Management

Machine learning is reshaping the way portfolios are built, monitored, and adjusted. Investors are no longer limited to ...

AI river forecasts may be accurate, but based on flawed logic

Artificial intelligence is changing how we predict river flow—but a new study led by researchers at the University of British ...

XRP Social Sentiment Hits “Extreme Negativity”—A Historical Setup for a Reversal?

XRP (CRYPTO: XRP) sits near $1.90 with sentiment hitting levels not seen since before its last major rally. The Crypto Fear & ...
EurekAlert!

LSTM resolves the long-standing trade-off between sensitivity and measurement range

Researchers in China propose and experimentally demonstrates a novel, deep learning-driven spectral recognition strategy, ...
IEEE

AdaBoost-LSTM: A Hybrid Ensemble Framework for Sequential Credit Card Fraud Detection

Abstract: Credit card fraud detection is a critical task in financial systems, requiring effective algorithms to accurately classify transactions as fraudulent or non-fraudulent. This paper proposes a ...
GitHub

Time Series Analysis with LSTM, ARIMA, Prophet, and Anomaly Detection

├── src/ # Source code modules │ ├── lstm_model.py # LSTM implementation with PyTorch │ ├── forecasting_models.py # ARIMA, Prophet, and statistical models │ ├── anomaly_detection.py # Anomaly ...
Scientific Research Publishing

Daungklang, P. and Sathidbhattarasombad, C. (2022) An Experimental Comparison of SES, DES, TES, ARIMA, and LSTM Algorithms for Short Time Series Prediction. Journal of Science ...

ABSTRACT: Forecasting fuel prices is a critical endeavor in energy economics, with significant implications for policy formulation, market regulation, and consumer decision-making. This study ...
C&EN

SOH Estimation Based on Multisource Feature Extraction and SSA-LSTM Algorithm

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. In contrast, data-driven methods do not rely on fixed models or ...
Frontiers

Cable laying conveyor state prediction and fault warning based on attention-driven CNN-LSTM algorithm

1 State Grid Wuxi Power Supply Company, Wuxi, China 2 Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China The monitoring condition of the cable laying conveyor, such ...
IEEE

A Novel Hybrid GCN-LSTM Algorithm for Energy Stock Price Prediction: Leveraging Temporal Dynamics and Inter-Stock Relationships

Abstract: Energy stock price prediction is a pivotal challenge in financial forecasting, characterized by high volatility and complexity influenced by geopolitical factors, regulatory shifts, and ...