hubtru/LTBoost
Boosted Hybrids of ensemble gradient algorithm for the long-term time series forecasting (LTSF)
This project helps operations managers, financial analysts, and supply chain planners make accurate long-term forecasts from complex, multi-variable historical data. It takes in structured time series data (like energy consumption, stock prices, or inventory levels) and produces predictions for future values, helping users anticipate trends and plan proactively. This is ideal for those who need to predict many steps ahead, not just the immediate future.
No commits in the last 6 months.
Use this if you need to generate highly accurate, long-range predictions for multiple interdependent data streams, outperforming traditional time series models.
Not ideal if your forecasting needs are for very short periods or involve only a single data stream without complex interdependencies.
Stars
17
Forks
1
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 28, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/hubtru/LTBoost"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
sktime/sktime
A unified framework for machine learning with time series
aeon-toolkit/aeon
A toolkit for time series machine learning and deep learning
Nixtla/neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms.
tslearn-team/tslearn
The machine learning toolkit for time series analysis in Python
Nixtla/mlforecast
Scalable machine 🤖 learning for time series forecasting.