MTSNet: Deep Probabilistic Cross-multivariate Time Series Modeling with External Factors for COVID-19
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 International Joint Conference on Neural Networks (IJCNN), 2023, 2023-June
- Issue Date:
- 2023-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| MTSNet_Deep_Probabilistic_Cross-multivariate_Time_Series_Modeling_with_External_Factors_for_COVID-19.pdf | Published version | 5.13 MB |
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Complex intelligent systems such as for tackling the COVID 19 pandemic involve multiple multivariate time series MTSs where both target variables such as COVID 19 infected confirmed and recovered cases and external factors such as virus mutation and infectivity vaccination and government intervention influence are coupled Forecasting such MTSs with multiple external MTS factors needs to model both within and between MTS interactions and handle their uncertainty heterogeneity and dynamics Existing shallow to deep MTS modelers including regressors deep recurrent neural networks such as DeepAR deep state space models and deep factor models do not jointly characterize these issues in a probabilistic manner across MTSs We propose an end to end deep probabilistic cross MTS learning network MTSNet MTSNet incorporates a tensor input with scaled target and external MTSs It then vertically and horizontally stacks long short memory networks for encoding and decoding target MTSs and enhances uncertainty modeling generalization and forecasting robustness by residual connection variational zoneout and probabilistic forecasting The tensor input is projected to a probability distribution for target MTS forecasting MTSNet outperforms the SOTA deep probabilistic MTS networks in forecasting COVID 19 confirmed cases and ICU patient numbers for six countries by involving virus mutation vaccination government interventions and infectivity
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