Sequential Aggregation of Heterogeneous Experts for PM10 Forecasting

Within the framework of air quality monitoring in Normandy, we experiment the methods of sequential aggregation for forecasting concentrations of PM10 of the next day. Besides the field of application and the adaptation to the special context of the work of the forecaster, the main originality of this work is that the set of experts contains at the same time statistical models built by means of various methods and groups of predictors, as well as experts which are deterministic chemical models of prediction modeling pollution, weather and atmosphere. Numerical results on recent data from April 2013 until March 2015, on three monitoring stations, illustrate and compare various methods of aggregation. The obtained results show that such a strategy improves clearly the performances of the best expert both in errors and in alerts and reaches the “unbiasedness” of observed-forecasted scatterplot, which is especially difficult to obtain by usual methods. Joint work with Benjamin Auder (Univ. Paris-Sud Orsay, France), Michel Bobbia (Atmo Normandie, Rouen, France) and Bruno Portier (LMI., INSA Rouen, France). More details can be found in B. Auder, M. Bobbia, J-M. Poggi, B. Portier Sequential Aggregation of Heterogeneous Experts for PM10 Forecasting Atmospheric Pollution Research, 7, 1101-1109, 2016
  • Sequential Aggregation of Heterogeneous Experts for PM10 Forecasting
  • 2018-02-23T10:30:00-03:00
  • 2018-02-23T11:30:00-03:00
  • Within the framework of air quality monitoring in Normandy, we experiment the methods of sequential aggregation for forecasting concentrations of PM10 of the next day. Besides the field of application and the adaptation to the special context of the work of the forecaster, the main originality of this work is that the set of experts contains at the same time statistical models built by means of various methods and groups of predictors, as well as experts which are deterministic chemical models of prediction modeling pollution, weather and atmosphere. Numerical results on recent data from April 2013 until March 2015, on three monitoring stations, illustrate and compare various methods of aggregation. The obtained results show that such a strategy improves clearly the performances of the best expert both in errors and in alerts and reaches the “unbiasedness” of observed-forecasted scatterplot, which is especially difficult to obtain by usual methods. Joint work with Benjamin Auder (Univ. Paris-Sud Orsay, France), Michel Bobbia (Atmo Normandie, Rouen, France) and Bruno Portier (LMI., INSA Rouen, France). More details can be found in B. Auder, M. Bobbia, J-M. Poggi, B. Portier Sequential Aggregation of Heterogeneous Experts for PM10 Forecasting Atmospheric Pollution Research, 7, 1101-1109, 2016
  • Cuándo 23/02/2018 de 10:30 a 11:30 (America/Montevideo / UTC-300)
  • Dónde Salón de Seminarios. Centro de Matemática
  • Nombre
  • Speaker Jean-Michel Poggi
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