IEEE

ML2++: A Model-Driven Blended Framework for Automated Machine Learning, Time Series Forecasting, and Data Visualization in IoT Applications

Abstract: Intelligent Internet of Things applications must seamlessly combine established software engineering practices with various machine learning (ML) and time series forecasting techniques.
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 ...
Hosted on MSN

Birth Rate Crisis: Why We May Need 3 Kids Per Family Just To Survive

Families need at least 2.7 children to avoid extinction—far higher than the conventional 2.1 replacement level fertility rate due to random variations in offspring numbers. With fertility rates below ...
Frontiers

Community Impact on a Cryptocurrency: Twitter Comparison Example Between Dogecoin and Litecoin

Context: The third generation of cryptocurrencies gathers cryptocurrencies that are as diverse as the market is big (e.g., Dogecoin or Litecoin). While Dogecoin is seen as a memecoin, the other ...