Sparse autoregression with exact mixed-integer optimization and scalable temporal/spatial extensions quantifies dominant periodic lags in large mobility and climate time series.
Discovering dynamic patterns from spatiotemporal data with time-varying low-rank autoregression,
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Interpretable Time Series Autoregression for Periodicity Quantification
Sparse autoregression with exact mixed-integer optimization and scalable temporal/spatial extensions quantifies dominant periodic lags in large mobility and climate time series.