salesforce/ETSformer
PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
This project offers a sophisticated method to predict future trends using historical data. It takes in time-series data, like sales figures or stock prices over time, and outputs highly accurate forecasts. This is ideal for data scientists, financial analysts, or operations managers who need precise long-term predictions to guide strategic decisions.
306 stars. No commits in the last 6 months.
Use this if you need to generate highly accurate, long-term forecasts from complex time-series data.
Not ideal if you need a simple, quick forecasting model or lack the technical expertise to work with advanced machine learning frameworks.
Stars
306
Forks
45
Language
Python
License
BSD-3-Clause
Category
Last pushed
May 10, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/salesforce/ETSformer"
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...