yotambraun/APDTFlow
APDTFlow is a modern and extensible forecasting framework for time series data that leverages advanced techniques including neural ordinary differential equations (Neural ODEs), transformer-based components, and probabilistic modeling. Its modular design allows researchers and practitioners to experiment with multiple forecasting models and easily
This project helps business analysts, operations managers, and data scientists accurately predict future trends and values for critical business metrics like sales, energy consumption, or financial data. It takes your historical time series data, including any relevant external factors like weather or promotions, and outputs precise forecasts with reliable confidence intervals. This tool is designed for anyone who needs to make data-driven decisions based on future predictions, even with irregular or incomplete data.
Available on PyPI.
Use this if you need highly accurate, production-ready forecasts with guaranteed confidence intervals for critical business planning, risk management, or operational optimization.
Not ideal if you need a simple, quick forecast for very short-term or less critical applications that don't require advanced modeling or rigorous uncertainty quantification.
Stars
43
Forks
5
Language
Python
License
MIT
Category
Last pushed
Nov 12, 2025
Commits (30d)
0
Dependencies
11
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