SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion

• URL: http://arxiv.org/abs/2404.14197v3
• Date: Mon, 18 Nov 2024 02:41:59 GMT
• Title: SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion
• Authors: Lu Han, Xu-Yang Chen, Han-Jia Ye, De-Chuan Zhan,
• Abstract summary: Time series forecasting plays a crucial role in various fields such as finance, traffic management, energy, and healthcare. Several methods utilize mechanisms like attention or mixer to address this by capturing channel correlations. This paper presents an efficient-based model, the Series-cOre Fused Time Series forecaster (SOFTS)
• Score: 59.96233305733875
• License:
• Abstract: Multivariate time series forecasting plays a crucial role in various fields such as finance, traffic management, energy, and healthcare. Recent studies have highlighted the advantages of channel independence to resist distribution drift but neglect channel correlations, limiting further enhancements. Several methods utilize mechanisms like attention or mixer to address this by capturing channel correlations, but they either introduce excessive complexity or rely too heavily on the correlation to achieve satisfactory results under distribution drifts, particularly with a large number of channels. Addressing this gap, this paper presents an efficient MLP-based model, the Series-cOre Fused Time Series forecaster (SOFTS), which incorporates a novel STar Aggregate-Redistribute (STAR) module. Unlike traditional approaches that manage channel interactions through distributed structures, \textit{e.g.}, attention, STAR employs a centralized strategy to improve efficiency and reduce reliance on the quality of each channel. It aggregates all series to form a global core representation, which is then dispatched and fused with individual series representations to facilitate channel interactions effectively.SOFTS achieves superior performance over existing state-of-the-art methods with only linear complexity. The broad applicability of the STAR module across different forecasting models is also demonstrated empirically. For further research and development, we have made our code publicly available at https://github.com/Secilia-Cxy/SOFTS.

Related papers

• Channel-Aware Low-Rank Adaptation in Time Series Forecasting [43.684035409535696]
  Two representative channel strategies are closely associated with model expressivity and robustness. We present a channel-aware low-rank adaptation method to condition CD models on identity-aware individual components.
  arXiv  Detail & Related papers  (2024-07-24T13:05:17Z)
• From Similarity to Superiority: Channel Clustering for Time Series Forecasting [61.96777031937871]
  We develop a novel and adaptable Channel Clustering Module ( CCM) CCM dynamically groups channels characterized by intrinsic similarities and leverages cluster information instead of individual channel identities. CCM can boost the performance of CI and CD models by an average margin of 2.4% and 7.2% on long-term and short-term forecasting, respectively.
  arXiv  Detail & Related papers  (2024-03-31T02:46:27Z)
• MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer [8.329947472853029]
  Channel Independence (CI) strategy treats all channels as a single channel, expanding the dataset. Mixed Channels strategy combines the data expansion advantages of the CI strategy with the ability to counteract inter-channel correlation forgetting. Model blends a specific number of channels, leveraging an attention mechanism to effectively capture inter-channel correlation information.
  arXiv  Detail & Related papers  (2024-03-14T09:43:07Z)
• Enhancing Multivariate Time Series Forecasting with Mutual Information-driven Cross-Variable and Temporal Modeling [24.041263835195423]
  We introduce the Cross-variable Decorrelation Aware feature Modeling (CDAM) for Channel-mixing approaches. We also introduce the Temporal correlation Aware Modeling (TAM) to exploit temporal correlations, a step beyond conventional single-step forecasting methods. Our novel framework significantly surpasses existing models, including those previously considered state-of-the-art, in comprehensive tests.
  arXiv  Detail & Related papers  (2024-03-01T04:42:47Z)
• Dance of Channel and Sequence: An Efficient Attention-Based Approach for Multivariate Time Series Forecasting [3.372816393214188]
  CSformer is an innovative framework characterized by a meticulously engineered two-stage self-attention mechanism. We introduce sequence adapters and channel adapters, ensuring the model's ability to discern salient features across various dimensions.
  arXiv  Detail & Related papers  (2023-12-11T09:10:38Z)
• Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models [61.10851158749843]
  Key insights can be obtained by discovering lead-lag relationships inherent in the data. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models.
  arXiv  Detail & Related papers  (2023-05-11T10:30:35Z)
• The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting [50.48888534815361]
  We show that models trained with the Channel Independent (CI) strategy outperform those trained with the Channel Dependent (CD) strategy. Our results conclude that the CD approach has higher capacity but often lacks robustness to accurately predict distributionally drifted time series. We propose a modified CD method called Predict Residuals with Regularization (PRReg) that can surpass the CI strategy.
  arXiv  Detail & Related papers  (2023-04-11T13:15:33Z)
• Channelized Axial Attention for Semantic Segmentation [70.14921019774793]
  We propose the Channelized Axial Attention (CAA) to seamlessly integratechannel attention and axial attention with reduced computationalcomplexity. Our CAA not onlyrequires much less computation resources compared with otherdual attention models such as DANet, but also outperforms the state-of-the-art ResNet-101-based segmentation models on alltested datasets.
  arXiv  Detail & Related papers  (2021-01-19T03:08:03Z)
• Deep Multimodal Fusion by Channel Exchanging [87.40768169300898]
  This paper proposes a parameter-free multimodal fusion framework that dynamically exchanges channels between sub-networks of different modalities. The validity of such exchanging process is also guaranteed by sharing convolutional filters yet keeping separate BN layers across modalities, which, as an add-on benefit, allows our multimodal architecture to be almost as compact as a unimodal network.
  arXiv  Detail & Related papers  (2020-11-10T09:53:20Z)

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.