Autoformer goes beyond the Transformer family and achieves the series-wise connection for the first time. In long-term forecasting, Autoformer achieves SOTA, with a 38% relative improvement on six …

Jun 24, 2021 · Going beyond Transformers, we design Autoformer as a novel decomposition architecture with an Auto-Correlation mechanism. We break with the pre-processing convention of …

针对长时序列预测中的复杂时间模式难以处理与运算效率高的问题，我们提出了基于深度分解架构和自相关机制的Autoformer模型。

Going beyond Transformers, we design Autoformer as a novel decomposition architecture with an Auto-Correlation mechanism. We break with the pre-processing convention of series decomposition and …

Going beyond Transformers, we design Autoformer as a novel decomposition architecture with an Auto-Correlation mechanism. We break with the pre-processing convention of series decomposition and …

Mar 24, 2025 · Autoformer introduces a groundbreaking approach to long-term time series forecasting by embedding decomposition and auto-correlation directly into the Transformer framework.

Enlighted by the classic time series analysis and stochastic process theory, we propose the Autoformer as a general series forecasting model [paper]. Autoformer goes beyond the Transformer family and …

Apr 20, 2025 · This guide provides practical instructions for getting started with training and evaluating Autoformer models for time series forecasting. It covers the basic workflow from setup to running …

Jun 16, 2023 · Autoformer builds upon the traditional method of decomposing time series into seasonality and trend-cycle components. This is achieved through the incorporation of a …

Our Autoformer harnesses the decomposition as an inner block of deep models, which can progressively decompose the hidden series throughout the whole forecasting process, including both …