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Azienda News:
- iTransformer: Inverted Transformers Are Effective for Time Series . . .
In this work, we reflect on the competent duties of Transformer components and repurpose the Transformer architecture without any modification to the basic components We propose iTransformer that simply applies the attention and feed-forward network on the inverted dimensions
- ITRANSFORMER: INVERTED TRANSFORMERS ARE EFFECTIVE FOR TIME SERIES . . .
We thoroughly evaluate the proposed iTransformer on various time series forecasting applications, validate the generality of the proposed framework and further dive into the effectiveness
- iTransformer: Inverted Transformers Are Effective for Time Series . . .
A minimalist, fundamental Transformer-based architecture for forecasting univariate time series is provided, providing a solid baseline that is simple to achieve but exhibits a stable forecasting ability not far behind that of state-of-the-art specialized designs
- GitHub - thuml iTransformer: Official implementation for iTransformer . . .
By introducing the proposed framework, Transformer and its variants achieve significant performance improvement, demonstrating the generality of the iTransformer approach and benefiting from efficient attention mechanisms
- iTransformer: Inverted Transformers Are Effective for Time Series . . .
TL;DR: Based on the reflection on the duties of Transformer components, we propose inverted Transformer for time series forecasting, which achieves the SOTA in real-world applications and shows powerful strength on framework generalization
- 最新研究iTransformer: Transformer不适合多变量时序预测？
在普通transformer中，构成token的同一时间点的多个变量可能会错位，并且过于局部化，无法为预测提供足够的信息。 在转置版本中，FFN利用的是每个变量token的序列表示。 根据通用近似定理，它们可以提取复杂的表示来描述时间序列。
- iTransformer：时间序列预测的革命性创新 - CSDN博客
iTransformer是一个基于Transformer架构的时间序列预测框架，它通过创新的"倒置"设计解决了传统Transformer在处理多变量时间序列数据时的痛点。 该项目源自论文《iTransformer: Inverted Transformers Are Effective for Time Series Forecasting》，并已被ICLR 2024收录为Spotlight论文。
- iTransformer: Inverted Transformers Are Effective for Time Series . . .
In this work, we reflect on the competent duties of Transformer components and repurpose the Transformer architecture without any modification to the basic components We propose iTransformer that simply applies the attention and feed-forward network on the inverted dimensions
- iTransformer: Inverted Transformers Are Effective for Time Series . . .
In this work, we reflect on the competent duties of Transformer components and repurpose the Transformer architecture without any adaptation on the basic components We propose iTransformer that simply inverts the duties of the attention mechanism and the feed-forward network
- iTransformer: Inverted Transformers Are Effective for Time Series . . .
These forecasters leverage Transformers to model the global dependencies over temporal tokens of time series, with each token formed by multiple variates of the same timestamp
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