mikekeith52/scalecast
The practitioner's forecasting library
This tool helps forecasters predict future values from time-based data. You input historical data points and their corresponding dates, and it outputs future forecasts along with evaluations of different forecasting models. It's designed for data analysts, researchers, or anyone needing to make accurate predictions from time series, especially when data is messy or complex.
350 stars. Used by 1 other package. Available on PyPI.
Use this if you need to quickly experiment with various forecasting models, handle missing data, or create robust predictions for single or multiple time series.
Not ideal if you're looking for a simple, single-model forecasting solution without needing to compare many advanced techniques or customize pipelines.
Stars
350
Forks
40
Language
Python
License
MIT
Category
Last pushed
Mar 08, 2026
Commits (30d)
0
Dependencies
14
Reverse dependents
1
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mikekeith52/scalecast"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
aeon-toolkit/aeon
A toolkit for time series machine learning and deep learning
sktime/sktime
A unified framework for machine learning with time series
Nixtla/neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms.
tslearn-team/tslearn
The machine learning toolkit for time series analysis in Python
Nixtla/statsforecast
Lightning ⚡️ fast forecasting with statistical and econometric models.