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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 ...
BMJ

Global, regional, and national characteristics of the main causes of increased disease burden due to the covid-19 pandemic: time-series modelling analysis of global burden of ...

Absolute and relative rate differences were calculated, along with their 95% confidence intervals (95% CIs), between the observed and expected rates for 174 causes of increases in incidence, ...
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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 ...
marktechpost

Darts: A New Python Library for User-Friendly Forecasting and Anomaly Detection on Time Series

Time series data, representing observations recorded sequentially over time, permeate various aspects of nature and business, from weather patterns and heartbeats to stock prices and production ...