The Average Guys Outsmarting Wall Street on Prediction Markets

How prediction market “sharps” have made millions wagering on everything from war to Rotten Tomatoes. Credit...Illustration ...
Hosted on MSN

Operationalizing Azure ML Forecasting with Datadog APM Visibility in 2026

In 2026, Azure Machine Learning has evolved from a sandbox for data scientists into a robust platform for operational forecasting, yet many teams still struggle to see what happens after deployment.
The New York Times

How to Watch the Lyrids Streak Across the Night Sky

Active since last week, the Lyrids meteor shower is expected to reach peak viewing tonight. By Katrina Miller Katrina Miller saw several meteors streaking across the night sky in the Greater Big Bend ...
GitHub

A comprehensive Python package for hierarchical time series forecasting with support for multiple forecasting methods, reconciliation techniques, and evaluation metrics.

This project provides a modern, well-structured implementation of hierarchical time series forecasting methods. It supports various forecasting algorithms (ARIMA, Prophet, LSTM) and reconciliation ...
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 ...
IEEE

A Hybrid Minkowski-Log-Cosh Loss Function for Robust LSTM-Based Time Series Forecasting

Designing robust loss functions is essential for improving deep learning performance on noisy and sparse time series. We propose the Minkowski–Log–Cosh (MLC) loss, a hybrid formulation that integrates ...
Scientific Research Publishing

Time-Series Forecasting of Solar Photovoltaic Power Generation in Malaysia ()

1 Faculty of Electrical Technology and Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia. 2 Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia ...
IEEE

Vision-Based Time Series Crowd Forecasting Using Semi-Supervised Learning

Abstract: Crowd forecasting is a crucial component of public safety, urban planning, and event management, enabling proactive decision-making based on anticipated crowd dynamics. Traditional ...
Frontiers

A hybrid LSTM–transformer model for accurate remaining useful life prediction of lithium-ion batteries

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 ...