المفاهيم الأساسية
Inverting the Transformer structure enhances time series forecasting by capturing multivariate correlations and improving series representations.
الملخص
The content discusses the iTransformer model, which repurposes the Transformer architecture without modifications. It focuses on capturing multivariate correlations and improving series representations for time series forecasting. The model achieves state-of-the-art performance on real-world datasets, showcasing its effectiveness in enhancing forecasting capabilities.
Structure:
- Introduction to iTransformer and its motivation.
- Challenges with traditional Transformer-based forecasters.
- Proposed iTransformer architecture overview.
- Experiments evaluating iTransformer's performance on various datasets.
- Analysis of model components and their impact on forecasting results.
الإحصائيات
ETT: 7 factors of electricity transformer from July 2016 to July 2018.
Exchange: Daily exchange rates from 8 countries from 1990 to 2016.
Weather: Meteorological factors collected every 10 minutes in 2020.
ECL: Hourly electricity consumption data of 321 clients.
Traffic: Hourly road occupancy rates measured by 862 sensors in San Francisco Bay area freeways.
Solar-Energy: Solar power production data of 137 PV plants in 2006 sampled every 10 minutes.
PEMS: Public traffic network data in California collected by 5-minute windows.
اقتباسات
"In this work, we reflect on the competent duties of Transformer components and repurpose the Transformer architecture without any modification to the basic components."
"Our contributions lie in three aspects..."
"Experimentally, the proposed iTransformer achieves state-of-the-art performance on real-world forecasting benchmarks."