salesforce/CoST
PyTorch code for CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting (ICLR 2022)
This project offers a sophisticated method for forecasting future trends based on historical data. It takes in various types of time-series data, such as electricity load diagrams or sales figures, and outputs highly accurate predictions by separating seasonal patterns from overall trends. It is designed for data scientists or researchers who need to develop advanced forecasting models.
235 stars. No commits in the last 6 months.
Use this if you are a data scientist or machine learning researcher looking to implement a state-of-the-art time series forecasting model with disentangled seasonal and trend components.
Not ideal if you are an end-user seeking a ready-to-use application or a developer looking for a plug-and-play library without deep learning expertise.
Stars
235
Forks
43
Language
Python
License
BSD-3-Clause
Category
Last pushed
Mar 22, 2023
Commits (30d)
0
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