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University of Toronto, University of Oxford, Vector Institute
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State Space Models can now generate time series with provable universality, outperforming fixed-grid models, especially on irregular data.
Time-series forecasting benchmarks are giving deep learning models undeserved credit, as simpler classical methods often perform just as well on datasets with strong periodicities.
