dongbeank/CATS
[NeurIPS 2024] Official implementation of the paper "Are Self-Attentions Effective for Time Series Forecasting?"
This project offers a highly efficient and accurate method for predicting future trends based on historical data. It takes in sequences of past observations, like sales figures or sensor readings, and outputs precise forecasts for upcoming periods. This is ideal for data scientists, machine learning engineers, and researchers working with time-series data.
No commits in the last 6 months.
Use this if you need to forecast long time series data with high accuracy while keeping computational resources (time and memory) low.
Not ideal if your forecasting needs are simple and short-term, where the overhead of a transformer model might be unnecessary.
Stars
63
Forks
11
Language
Python
License
MIT
Category
Last pushed
Dec 31, 2024
Commits (30d)
0
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