DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2405.08440v1
• Date: Tue, 14 May 2024 09:01:33 GMT
• Title: DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting
• Authors: Qinshuo Liu, Yanwen Fang, Pengtao Jiang, Guodong Li,
• Abstract summary: This paper proposes the Deep Graph Clustering Transformer (DGCformer) for multivariate time series forecasting. It first groups these relevant variables by a graph convolutional network integrated with an autoencoder, and a former-latter masked self-attention mechanism is then considered.
• Score: 16.081071155397186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multivariate time series forecasting tasks are usually conducted in a channel-dependent (CD) way since it can incorporate more variable-relevant information. However, it may also involve a lot of irrelevant variables, and this even leads to worse performance than the channel-independent (CI) strategy. This paper combines the strengths of both strategies and proposes the Deep Graph Clustering Transformer (DGCformer) for multivariate time series forecasting. Specifically, it first groups these relevant variables by a graph convolutional network integrated with an autoencoder, and a former-latter masked self-attention mechanism is then considered with the CD strategy being applied to each group of variables while the CI one for different groups. Extensive experimental results on eight datasets demonstrate the superiority of our method against state-of-the-art models, and our code will be publicly available upon acceptance.

Related papers

• DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting [43.071713191702486]
  DisenTS is a tailored framework for modeling disentangled channel evolving patterns in general time series forecasting. We introduce a novel Forecaster Aware Gate (FAG) module that generates the routing signals adaptively according to both the forecasters' states and input series' characteristics.
  arXiv  Detail & Related papers  (2024-10-30T12:46:14Z)
• Timer-XL: Long-Context Transformers for Unified Time Series Forecasting [67.83502953961505]
  We present Timer-XL, a generative Transformer for unified time series forecasting. Timer-XL achieves state-of-the-art performance across challenging forecasting benchmarks through a unified approach.
  arXiv  Detail & Related papers  (2024-10-07T07:27:39Z)
• VE: Modeling Multivariate Time Series Correlation with Variate Embedding [0.4893345190925178]
  Current channel-independent (CI) models and models with a CI final projection layer are unable to capture correlations. We present the variate embedding (VE) pipeline, which learns a unique and consistent embedding for each variate. The VE pipeline can be integrated into any model with a CI final projection layer to improve multivariate forecasting.
  arXiv  Detail & Related papers  (2024-09-10T02:49:30Z)
• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks [9.006068771300377]
  We present ForecastGrapher, a framework for capturing the intricate temporal dynamics and inter-series correlations. Our approach is underpinned by three pivotal steps: generating custom node embeddings to reflect the temporal variations within each series; constructing an adaptive adjacency matrix to encode the inter-series correlations; and thirdly, augmenting the GNNs' expressive power by diversifying the node feature distribution.
  arXiv  Detail & Related papers  (2024-05-28T10:40:20Z)
• Unified Training of Universal Time Series Forecasting Transformers [104.56318980466742]
  We present a Masked-based Universal Time Series Forecasting Transformer (Moirai) Moirai is trained on our newly introduced Large-scale Open Time Series Archive (LOTSA) featuring over 27B observations across nine domains. Moirai achieves competitive or superior performance as a zero-shot forecaster when compared to full-shot models.
  arXiv  Detail & Related papers  (2024-02-04T20:00:45Z)
• 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)
• Expressing Multivariate Time Series as Graphs with Time Series Attention Transformer [14.172091921813065]
  We propose the Time Series Attention Transformer (TSAT) for multivariate time series representation learning. Using TSAT, we represent both temporal information and inter-dependencies of time series in terms of edge-enhanced dynamic graphs. We show that TSAT clearly outerperforms six state-of-the-art baseline methods in various forecasting horizons.
  arXiv  Detail & Related papers  (2022-08-19T12:25:56Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)

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.