• Corpus ID: 150373780

Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting

@inproceedings{Sen2019ThinkGA,
  title={Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting},
  author={Rajat Sen and Hsiang-Fu Yu and Inderjit S. Dhillon},
  booktitle={NeurIPS},
  year={2019}
}
Forecasting high-dimensional time series plays a crucial role in many applications such as demand forecasting and financial predictions. [] Key Method In particular, DeepGLO is a hybrid model that combines a global matrix factorization model regularized by a temporal deep network with a local deep temporal model that captures patterns specific to each dimension. The global and local models are combined via a data-driven attention mechanism for each dimension.

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