Hierarchical Datasets

Here we host a collection of datasets used in previous hierarchical research by Rangapuram et al. [2021], Olivares et al. [2023], and Kamarthi et al. [2022]. The benchmark datasets utilized include Australian Monthly Labour (Labour), SF Bay Area daily Traffic (Traffic, OldTraffic), Quarterly Australian Tourism Visits (TourismSmall), Monthly Australian Tourism visits (TourismLarge, OldTourismLarge), and daily Wikipedia article views (Wiki2). Old datasets favor the original datasets with minimal target variable preprocessing (Rangapuram et al. [2021], Olivares et al. [2023]), while the remaining datasets follow PROFHIT experimental settings.

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References

• Syama Sundar Rangapuram, Lucien D Werner, Konstantinos Benidis, Pedro Mercado, Jan Gasthaus, Tim Januschowski. (2021). “End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series”. Proceedings of the 38th International Conference on Machine Learning (ICML).
  
• Kin G. Olivares, O. Nganba Meetei, Ruijun Ma, Rohan Reddy, Mengfei Cao, Lee Dicker (2022).”Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures”. International Journal Forecasting, special issue.
  
• Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodriguez, Chao Zhang, and B. Prakash. PROFHIT: Probabilistic robust forecasting for hierarchical time-series. Computing Research Repository.URL https://arxiv.org/abs/2206.07940.
  

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Labour

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Labour (freq:str='MS', horizon:int=8, papers_horizon:int=12,
         seasonality:int=12, test_size:int=125,
         tags_names:Tuple[str]=('Country', 'Country/Region',
         'Country/Gender/Region', 'Country/Employment/Gender/Region'))

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TourismLarge

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TourismLarge (freq:str='MS', horizon:int=12, papers_horizon:int=12,
               seasonality:int=12, test_size:int=57,
               tags_names:Tuple[str]=('Country', 'Country/State',
               'Country/State/Zone', 'Country/State/Zone/Region',
               'Country/Purpose', 'Country/State/Purpose',
               'Country/State/Zone/Purpose',
               'Country/State/Zone/Region/Purpose'))

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TourismSmall

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TourismSmall (freq:str='Q', horizon:int=4, papers_horizon:int=4,
               seasonality:int=4, test_size:int=9,
               tags_names:Tuple[str]=('Country', 'Country/Purpose',
               'Country/Purpose/State',
               'Country/Purpose/State/CityNonCity'))

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Traffic

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Traffic (freq:str='D', horizon:int=14, papers_horizon:int=7,
          seasonality:int=7, test_size:int=91,
          tags_names:Tuple[str]=('Level1', 'Level2', 'Level3', 'Level4'))

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Wiki2

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Wiki2 (freq:str='D', horizon:int=14, papers_horizon:int=7,
        seasonality:int=7, test_size:int=91,
        tags_names:Tuple[str]=('Views', 'Views/Country',
        'Views/Country/Access', 'Views/Country/Access/Agent',
        'Views/Country/Access/Agent/Topic'))

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OldTraffic

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OldTraffic (freq:str='D', horizon:int=1, papers_horizon:int=1,
             seasonality:int=7, test_size:int=91,
             tags_names:Tuple[str]=('Level1', 'Level2', 'Level3',
             'Level4'))

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OldTourismLarge

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OldTourismLarge (freq:str='MS', horizon:int=12, papers_horizon:int=12,
                  seasonality:int=12, test_size:int=57,
                  tags_names:Tuple[str]=('Country', 'Country/State',
                  'Country/State/Zone', 'Country/State/Zone/Region',
                  'Country/Purpose', 'Country/State/Purpose',
                  'Country/State/Zone/Purpose',
                  'Country/State/Zone/Region/Purpose'))

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HierarchicalData

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HierarchicalData ()

Initialize self. See help(type(self)) for accurate signature.