Datasets › Multivariate-Mobility-Paris

Multivariate-Mobility-Paris

Introduced by Héber H. Arcolezi et al. in Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?1 May 2022 archive 2025-07-28

The original dataset was provided by Orange telecom in France, which contains anonymized and aggregated human mobility data. The Multivariate-Mobility-Paris dataset comprises information from 2020-08-24 to 2020-11-04 (72 days during the COVID-19 pandemic), with time granularity of 30 minutes and spatial granularity of 6 coarse regions in Paris, France. In other words, it represents a multivariate time series dataset.

This dataset can be used for several time-series tasks such as univariate/multivariate forecasting/classification with classic, machine learning, and privacy-preserving machine learning techniques.

Benchmarks archive 2025-07-28

No leaderboard in the archive resolves to this dataset.

Papers archive 2025-07-28

No paper in the archive has a leaderboard row on this dataset; the archive counts 2 papers for it but never published that list.

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

MIT

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • Multivariate-Mobility-Paris

1 variant name, as the archive lists them.

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