Thinklab-SJTU/Crossformer
Official implementation of our ICLR 2023 paper "Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting"
This tool helps forecasters and analysts predict future values from complex, multi-faceted time-series data. You input historical records, like sensor readings from multiple locations or various financial indicators over time, and it outputs precise forecasts for upcoming periods. It is designed for anyone needing accurate predictions from numerous interconnected data streams.
669 stars. No commits in the last 6 months.
Use this if you need to accurately predict future trends for systems with many interacting variables, like energy consumption across different power plants or stock prices for an entire market sector.
Not ideal if your data is a simple, single time series, or if you require real-time, ultra-low-latency predictions on streaming data.
Stars
669
Forks
107
Language
Python
License
Apache-2.0
Category
Last pushed
Dec 01, 2023
Commits (30d)
0
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