A sparse, non-negative autoregression with coefficients summing to one quantifies weekly periodicity in urban mobility, showing pandemic disruption and recovery across cities and travel modes.
Correlating time series with interpretable convolutional kernels.IEEE Transactions on Knowledge and Data Engineering, 37(6):3272–3283, 2025b
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Data-Driven Discovery of Mobility Periodicity for Understanding Urban Systems
A sparse, non-negative autoregression with coefficients summing to one quantifies weekly periodicity in urban mobility, showing pandemic disruption and recovery across cities and travel modes.