Deep Spectral Copula Mechanisms Modeling Coupled and Volatile Multivariate Time Series
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023, 00, pp. 1-10
- Issue Date:
- 2023-11-06
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Deep_Spectral_Copula_Mechanisms_Modeling_Coupled_and_Volatile_Multivariate_Time_Series.pdf | Published version | 5.66 MB |
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Exploring inter and intra time series relations and handling volatile covariates form various challenges in modeling Coupled and Volatile Multivariate Time Series CVMTS A typical CVMTS data is the COVID 19 case time series across multiple countries whose covariates may involve high volatility caused by missing samples The existing approaches merely focus on a single set of multivariate time series or multiple multivariate time series without considering their volatile temporal covariates They do not sufficiently characterize CVMTS features by explicitly modeling intra and inter MTS couplings and effectively handling volatile covariates in multiple multivariate time series Accordingly we propose Deep Spectral Copula Mechanisms DSCM to adapt CVMTS Specifically DSCM 1 incorporates a Singular Spectral Analysis SSA module to reduce the volatility of multiple covariates 2 applies an intra MTS coupling module to explicitly model the temporal couplings within a single set of multivariate time series and 3 transforms target variables into joint probability distributions by Gaussian copula transformation to establish inter MTS couplings across multiple multivariate time series Substantial experiments on COVID 19 time series data from multiple countries indicate the superiority of DSCM over state of the art approaches
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