Sachithx/EntroPE
This includes the codebase for EntroPE (Entropy-Guided Dynamic Patch Encoder for Time Series Forecasting) paper.
This project helps operations engineers, data scientists, and analysts predict future values in time-series data like energy consumption or weather patterns. Instead of breaking data into arbitrary chunks, it intelligently segments your raw time series into meaningful pieces based on how much the data changes. This allows for more accurate forecasts of critical metrics, improving planning and resource allocation.
Use this if you need to improve the accuracy of your time series forecasts, especially for complex or rapidly changing data, and want to incorporate the natural structure of your time series.
Not ideal if your time series data is very simple, changes predictably, or if you require an extremely lightweight solution where maximum predictive power is not the primary goal.
Stars
41
Forks
3
Language
Python
License
Apache-2.0
Category
Last pushed
Feb 01, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/Sachithx/EntroPE"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
amazon-science/chronos-forecasting
Chronos: Pretrained Models for Time Series Forecasting
SalesforceAIResearch/uni2ts
Unified Training of Universal Time Series Forecasting Transformers
moment-timeseries-foundation-model/moment
MOMENT: A Family of Open Time-series Foundation Models, ICML'24
ServiceNow/TACTiS
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series, from...
yotambraun/APDTFlow
APDTFlow is a modern and extensible forecasting framework for time series data that leverages...