TiVaT: A Transformer with a Single Unified Mechanism for Capturing Asynchronous Dependencies in Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2410.01531v2
• Date: Fri, 31 Jan 2025 02:32:39 GMT
• Title: TiVaT: A Transformer with a Single Unified Mechanism for Capturing Asynchronous Dependencies in Multivariate Time Series Forecasting
• Authors: Junwoo Ha, Hyukjae Kwon, Sungsoo Kim, Kisu Lee, Seungjae Park, Ha Young Kim,
• Abstract summary: TiVaT is a novel architecture incorporating a single unified module, a Joint-Axis (JA) attention module.<n>The JA attention module dynamically selects relevant features to particularly capture asynchronous interactions.<n>Extensive experiments demonstrate TiVaT's overall performance across diverse datasets.
• Score: 4.733959271565453
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multivariate time series (MTS) forecasting is vital across various domains but remains challenging due to the need to simultaneously model temporal and inter-variate dependencies. Existing channel-dependent models, where Transformer-based models dominate, process these dependencies separately, limiting their capacity to capture complex interactions such as lead-lag dynamics. To address this issue, we propose TiVaT (Time-variate Transformer), a novel architecture incorporating a single unified module, a Joint-Axis (JA) attention module, that concurrently processes temporal and variate modeling. The JA attention module dynamically selects relevant features to particularly capture asynchronous interactions. In addition, we introduce distance-aware time-variate sampling in the JA attention, a novel mechanism that extracts significant patterns through a learned 2D embedding space while reducing noise. Extensive experiments demonstrate TiVaT's overall performance across diverse datasets, particularly excelling in scenarios with intricate asynchronous dependencies.

Related papers

• TimeCNN: Refining Cross-Variable Interaction on Time Point for Time Series Forecasting [44.04862924157323]
  Transformer-based models demonstrate significant potential in modeling cross-time and cross-variable interaction. We propose a TimeCNN model to refine cross-variable interactions to enhance time series forecasting. Extensive experiments conducted on 12 real-world datasets demonstrate that TimeCNN consistently outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2024-10-07T09:16:58Z)
• TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model [11.281386703572842]
  TimeDiT is a diffusion transformer model that combines temporal dependency learning with probabilistic sampling. TimeDiT employs a unified masking mechanism to harmonize the training and inference process across diverse tasks. Our systematic evaluation demonstrates TimeDiT's effectiveness both in fundamental tasks, i.e., forecasting and imputation, through zero-shot/fine-tuning.
  arXiv  Detail & Related papers  (2024-09-03T22:31:57Z)
• UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting [98.12558945781693]
  We propose a transformer-based model UniTST containing a unified attention mechanism on the flattened patch tokens. Although our proposed model employs a simple architecture, it offers compelling performance as shown in our experiments on several datasets for time series forecasting.
  arXiv  Detail & Related papers  (2024-06-07T14:39:28Z)
• Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting [26.141054975797868]
  We propose a novel Adaptive Multi-Scale Decomposition (AMD) framework for time series forecasting (TSF) Our framework decomposes time series into distinct temporal patterns at multiple scales, leveraging the Multi-Scale Decomposable Mixing (MDM) block. Our approach effectively models both temporal and channel dependencies and utilizes autocorrelation to refine multi-scale data integration.
  arXiv  Detail & Related papers  (2024-06-06T05:27:33Z)
• VCformer: Variable Correlation Transformer with Inherent Lagged Correlation for Multivariate Time Series Forecasting [1.5165632546654102]
  We propose Variable Correlation Transformer (VCformer) to mine the correlations among variables. VCA calculates and integrates the cross-correlation scores corresponding to different lags between queries and keys. Inspired by Koopman dynamics theory, we also develop Koopman Temporal Detector (KTD) to better address the non-stationarity in time series.
  arXiv  Detail & Related papers  (2024-05-19T07:39:22Z)
• CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables [9.95711569148527]
  We introduce a method to Construct Auxiliary Time Series (CATS) that functions like a 2D temporal-contextual attention mechanism. Even with a basic 2-layer as core predictor, CATS achieves state-of-the-art, significantly reducing complexity and parameters compared to previous multivariate models.
  arXiv  Detail & Related papers  (2024-03-04T01:52:40Z)
• PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations [49.80959046861793]
  We present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers. Our experimentation across seven diversetemporal real-world LMTF datasets reveals that PDETime adapts effectively to the intrinsic nature of the data.
  arXiv  Detail & Related papers  (2024-02-25T17:39:44Z)
• FCDNet: Frequency-Guided Complementary Dependency Modeling for Multivariate Time-Series Forecasting [9.083469629116784]
  We propose FCDNet, a concise yet effective framework for time-series forecasting. It helps extract long- and short-term dependency information adaptively from multi-level frequency patterns. Experiments show that FCDNet significantly exceeds strong baselines.
  arXiv  Detail & Related papers  (2023-12-27T07:29:52Z)
• A Decoupled Spatio-Temporal Framework for Skeleton-based Action Segmentation [89.86345494602642]
  Existing methods are limited in weak-temporal modeling capability. We propose a Decoupled Scoupled Framework (DeST) to address the issues. DeST significantly outperforms current state-of-the-art methods with less computational complexity.
  arXiv  Detail & Related papers  (2023-12-10T09:11:39Z)
• Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data [50.84488941336865]
  We propose a novel method called Fully- Spatial-Temporal Graph Neural Network (FC-STGNN) For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances. For graph convolution, we devise FC graph convolution with a moving-pooling GNN layer to effectively capture the ST dependencies for learning effective representations.
  arXiv  Detail & Related papers  (2023-09-11T08:44:07Z)
• 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)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting [8.881348323807158]
  We propose a multi-scale adaptive graph neural network (MAGNN) to address the above issue. Experiments on four real-world datasets demonstrate that MAGNN outperforms the state-of-the-art methods across various settings.
  arXiv  Detail & Related papers  (2022-01-13T08:04:10Z)
• Deep Explicit Duration Switching Models for Time Series [84.33678003781908]
  We propose a flexible model that is capable of identifying both state- and time-dependent switching dynamics. State-dependent switching is enabled by a recurrent state-to-switch connection. An explicit duration count variable is used to improve the time-dependent switching behavior.
  arXiv  Detail & Related papers  (2021-10-26T17:35:21Z)
• Transformer Hawkes Process [79.16290557505211]
  We propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies. THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.
  arXiv  Detail & Related papers  (2020-02-21T13:48:13Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

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.