Abstract:
Vector autoregressive models provide a simple generative model for multivariate, time-series data. The autoregressive coefficients of the vector autoregressive model desc...Show MoreMetadata
Abstract:
Vector autoregressive models provide a simple generative model for multivariate, time-series data. The autoregressive coefficients of the vector autoregressive model describe a network process. However, in real-world applications such as macroeconomics or neuroimaging, time-series data arise not from isolated network processes but instead from the simultaneous occurrence of multiple network processes. Standard vector autoregressive models cannot provide insights about the underlying structure of such time-series data. In this work, we present the autoregressive linear mixture (ALM) model. The ALM proposes a decomposition of time-series data into co-occurring network processes that we call autoregressive components. We also present a non-convex likelihood-based estimator for fitting the ALM model and show that it can be solved using the proximal alternating linearized minimization (PALM) algorithm. We validate the ALM on both synthetic and real-world electroencephalography data, showing that we can disambiguate task-relevant autoregressive components that correspond with distinct network processes.
Published in: IEEE Transactions on Signal Processing ( Volume: 68)
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- IEEE Keywords
- Index Terms
- Mixture Model ,
- Network Process ,
- Mixture Of Processes ,
- Linear Mixture Model ,
- Standard Model ,
- Time Series Data ,
- Model Coefficients ,
- Real-world Data ,
- Autoregressive Model ,
- Vector Autoregressive Model ,
- Autoregressive Coefficients ,
- Autoregressive Component ,
- Model In Order ,
- Transfer Function ,
- Independent Component Analysis ,
- Least Squares Estimation ,
- Non-negative Matrix Factorization ,
- Maximum A Posteriori ,
- Sleep Stages ,
- Components In Order ,
- Number Of Realizations ,
- Mixing Coefficients ,
- Dictionary Learning ,
- Norm Penalty ,
- Negative Log-likelihood ,
- Autocovariance Function ,
- Decrease In The Likelihood ,
- Proximal Operator ,
- Multiple Realization ,
- Proximal Gradient
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Mixture Model ,
- Network Process ,
- Mixture Of Processes ,
- Linear Mixture Model ,
- Standard Model ,
- Time Series Data ,
- Model Coefficients ,
- Real-world Data ,
- Autoregressive Model ,
- Vector Autoregressive Model ,
- Autoregressive Coefficients ,
- Autoregressive Component ,
- Model In Order ,
- Transfer Function ,
- Independent Component Analysis ,
- Least Squares Estimation ,
- Non-negative Matrix Factorization ,
- Maximum A Posteriori ,
- Sleep Stages ,
- Components In Order ,
- Number Of Realizations ,
- Mixing Coefficients ,
- Dictionary Learning ,
- Norm Penalty ,
- Negative Log-likelihood ,
- Autocovariance Function ,
- Decrease In The Likelihood ,
- Proximal Operator ,
- Multiple Realization ,
- Proximal Gradient
- Author Keywords