MTSNet: Deep Probabilistic Cross-multivariate Time Series Modeling with External Factors for COVID-19

Yang, Yang; Cao, Longbing

doi:10.1109/ijcnn54540.2023.10191636

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Other1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Fourier Graph Convolution Transformer for Financial Multivariate Time Series Forecasting

Zhou,

Wang,

2024

2024 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

Fourier Graph Convolution Transformer for Financial Multivariate Time Series Forecasting

Zhou,

Wang,

2024

2024 International Joint Conference on Neural Networks (IJCNN)

View full text Add to dashboard Cite

Learning Informative Representation for Fairness-Aware Multivariate Time-Series Forecasting: A Group-Based Perspective

He,

Zhang,

Wang

et al. 2024

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

The non-stationary nature of real-world Multivariate Time Series (MTS) data presents forecasting models with a formidable challenge of the time-variant distribution of time series, referred to as distribution shift. Existing studies on the distribution shift mostly adhere to adaptive normalization techniques for alleviating temporal mean and covariance shifts or time-variant modeling for capturing temporal shifts. Despite improving model generalization, these normalization-based methods often assume a time-invariant transition between outputs and inputs but disregard specific intra-/inter-series correlations, while time-variant models overlook the intrinsic causes of the distribution shift. This limits model expressiveness and interpretability of tackling the distribution shift for MTS forecasting. To mitigate such a dilemma, we present a unified Probabilistic Graphical Model to Jointly capturing intra-/inter-series correlations and modeling the time-variant transitional distribution, and instantiate a neural framework called JointPGM for non-stationary MTS forecasting. Specifically, JointPGM first employs multiple Fourier basis functions to learn dynamic time factors and designs two distinct learners: intra-series and inter-series learners. The intraseries learner effectively captures temporal dynamics by utilizing temporal gates, while the inter-series learner explicitly models spatial dynamics through multi-hop propagation, incorporating Gumbel-softmax sampling. These two types of series dynamics are subsequently fused into a latent variable, which is inversely employed to infer time factors, generate final prediction, and perform reconstruction. We validate the effectiveness and efficiency of JointPGM through extensive experiments on six highly nonstationary MTS datasets, achieving state-of-the-art forecasting performance of MTS forecasting.

show abstract

Copula Variational LSTM for High-Dimensional Cross-Market Multivariate Dependence Modeling

Cao

2024

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

MTSNet: Deep Probabilistic Cross-multivariate Time Series Modeling with External Factors for COVID-19

Cited by 3 publications

References 36 publications

Fourier Graph Convolution Transformer for Financial Multivariate Time Series Forecasting

Fourier Graph Convolution Transformer for Financial Multivariate Time Series Forecasting

Learning Informative Representation for Fairness-Aware Multivariate Time-Series Forecasting: A Group-Based Perspective

Copula Variational LSTM for High-Dimensional Cross-Market Multivariate Dependence Modeling

Contact Info

Product

Resources

About