SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention

• URL: http://arxiv.org/abs/2402.10198v3
• Date: Mon, 3 Jun 2024 07:34:37 GMT
• Title: SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
• Authors: Romain Ilbert, Ambroise Odonnat, Vasilii Feofanov, Aladin Virmaux, Giuseppe Paolo, Themis Palpanas, Ievgen Redko,
• Abstract summary: We show that transformers are incapable of converging to their true solution despite their high expressive power. We propose a shallow lightweight transformer model that escapes bad local minima when optimized with sharpness-aware optimization. In particular, SAMformer surpasses current state-of-the-art methods and is on par with the biggest foundation model MOIRAI while having significantly fewer parameters.
• Score: 14.672072173674039
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multivariate long-term forecasting. To better understand this phenomenon, we start by studying a toy linear forecasting problem for which we show that transformers are incapable of converging to their true solution despite their high expressive power. We further identify the attention of transformers as being responsible for this low generalization capacity. Building upon this insight, we propose a shallow lightweight transformer model that successfully escapes bad local minima when optimized with sharpness-aware optimization. We empirically demonstrate that this result extends to all commonly used real-world multivariate time series datasets. In particular, SAMformer surpasses current state-of-the-art methods and is on par with the biggest foundation model MOIRAI while having significantly fewer parameters. The code is available at https://github.com/romilbert/samformer.

Related papers

• Are Self-Attentions Effective for Time Series Forecasting? [4.990206466948269]
  Time series forecasting is crucial for applications across multiple domains and various scenarios. Recent findings have indicated that simpler linear models might outperform complex Transformer-based approaches. We introduce a new architecture, Cross-Attention-only Time Series transformer (CATS), that rethinks the traditional Transformer framework.
  arXiv  Detail & Related papers  (2024-05-27T06:49:39Z)
• On the Convergence of Encoder-only Shallow Transformers [62.639819460956176]
  We build the global convergence theory of encoder-only shallow Transformers under a realistic setting. Our results can pave the way for a better understanding of modern Transformers, particularly on training dynamics.
  arXiv  Detail & Related papers  (2023-11-02T20:03:05Z)
• iTransformer: Inverted Transformers Are Effective for Time Series Forecasting [62.40166958002558]
  We propose iTransformer, which simply applies the attention and feed-forward network on the inverted dimensions. The iTransformer model achieves state-of-the-art on challenging real-world datasets.
  arXiv  Detail & Related papers  (2023-10-10T13:44:09Z)
• U-shaped Transformer: Retain High Frequency Context in Time Series Analysis [0.5710971447109949]
  In this paper, we consider the low-pass characteristics of transformers and try to incorporate the advantages of them. We introduce patch merge and split operation to extract features with different scales and use larger datasets to fully make use of the transformer backbone. Our experiments demonstrate that the model performs at an advanced level across multiple datasets with relatively low cost.
  arXiv  Detail & Related papers  (2023-07-18T07:15:26Z)
• Two Steps Forward and One Behind: Rethinking Time Series Forecasting with Deep Learning [7.967995669387532]
  The Transformer is a highly successful deep learning model that has revolutionised the world of artificial neural networks. We investigate the effectiveness of Transformer-based models applied to the domain of time series forecasting. We propose a set of alternative models that are better performing and significantly less complex.
  arXiv  Detail & Related papers  (2023-04-10T12:47:42Z)
• Towards Long-Term Time-Series Forecasting: Feature, Pattern, and Distribution [57.71199089609161]
  Long-term time-series forecasting (LTTF) has become a pressing demand in many applications, such as wind power supply planning. Transformer models have been adopted to deliver high prediction capacity because of the high computational self-attention mechanism. We propose an efficient Transformerbased model, named Conformer, which differentiates itself from existing methods for LTTF in three aspects.
  arXiv  Detail & Related papers  (2023-01-05T13:59:29Z)
• Robust representations of oil wells' intervals via sparse attention mechanism [2.604557228169423]
  We introduce the class of efficient Transformers named Regularized Transformers (Reguformers) The focus in our experiments is on oil&gas data, namely, well logs. To evaluate our models for such problems, we work with an industry-scale open dataset consisting of well logs of more than 20 wells.
  arXiv  Detail & Related papers  (2022-12-29T09:56:33Z)
• A K-variate Time Series Is Worth K Words: Evolution of the Vanilla Transformer Architecture for Long-term Multivariate Time Series Forecasting [52.33042819442005]
  Transformer has become the de facto solution for MTSF, especially for the long-term cases. In this study, we point out that the current tokenization strategy in MTSF Transformer architectures ignores the token inductive bias of Transformers. We make a series of evolution on the basic architecture of the vanilla MTSF transformer. Surprisingly, the evolved simple transformer architecture is highly effective, which successfully avoids the over-smoothing phenomena in the vanilla MTSF transformer.
  arXiv  Detail & Related papers  (2022-12-06T07:00:31Z)
• Visformer: The Vision-friendly Transformer [105.52122194322592]
  We propose a new architecture named Visformer, which is abbreviated from the Vision-friendly Transformer' With the same computational complexity, Visformer outperforms both the Transformer-based and convolution-based models in terms of ImageNet classification accuracy.
  arXiv  Detail & Related papers  (2021-04-26T13:13:03Z)
• Transformers Solve the Limited Receptive Field for Monocular Depth Prediction [82.90445525977904]
  We propose TransDepth, an architecture which benefits from both convolutional neural networks and transformers. This is the first paper which applies transformers into pixel-wise prediction problems involving continuous labels.
  arXiv  Detail & Related papers  (2021-03-22T18:00:13Z)
• NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting [24.510978166050293]
  This work is the first attempt to propose a Non-Autoregressive Transformer architecture for time series forecasting. We present a novel spatial-temporal attention mechanism, building a bridge by a learned temporal influence map to fill the gaps between the spatial and temporal attention.
  arXiv  Detail & Related papers  (2021-02-10T18:36:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.