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We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning. By parametrizing a per-time-series linear state space model with a jointly-learned recurrent neural network, our method retains desired properties of state space models such as data efficiency and interpretability, while making use of the ability to learn complex patterns from raw data offered by deep learning approaches. Our method scales gracefully from regimes where little training data is available to regimes where data from millions of time series can be leveraged to learn accurate models. We provide qualitative as well as quantitative results with the proposed method, showing that it compares favorably to the state-of-the-art.
Author Information
Syama Sundar Rangapuram (Amazon Research)
Matthias W Seeger (Amazon Development Center)
Jan Gasthaus (Amazon.com)
Lorenzo Stella (Amazon)
Bernie Wang (AWS AI Labs)
Tim Januschowski (Amazon)
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2020 Poster: Deep Rao-Blackwellised Particle Filters for Time Series Forecasting »
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2018 Poster: Scalable Hyperparameter Transfer Learning »
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2016 Poster: Bayesian Intermittent Demand Forecasting for Large Inventories »
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2016 Oral: Bayesian Intermittent Demand Forecasting for Large Inventories »
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