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Benzene Concentration Dataset

Chang Wei Tan; Christoph Bergmeir; Francois Petitjean; Geoffrey I Webb


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{
  "description": "<p>This dataset is part of the Monash, UEA &amp;&nbsp;UCR time series regression repository.&nbsp;<a href=\"http://tseregression.org/\">http://tseregression.org/</a></p>\n\n<p>This goal of this dataset is to predict benzene concentration in an Italian city. This dataset contains 8878 time series obtained from the Air Quality dataset from the UCI repository.&nbsp;The time series has 8 dimensions which consists of hourly averaged responses from an array of 5 metal oxide chemical sensors embedded in an Air Quality Chemical Multisensor Device, as well as temperature, relative humidity and absolute humidity.&nbsp;The Air Quality Chemical Multisensor device was located on the field in a significantly polluted area, at road level, within an Italian city.&nbsp;Data were recorded from March 2004 to February 2005 (one year) representing the longest freely available recordings of on field deployed air quality chemical sensor devices responses.&nbsp;Ground Truth hourly averaged concentrations for CO, Non Metanic Hydrocarbons, Benzene, Total Nitrogen Oxides (NOx) and Nitrogen Dioxide (NO2) and were provided by a co-located reference certified analyzer.<br>\n<br>\nPlease refer to <a href=\"https://archive.ics.uci.edu/ml/datasets/Air+Quality\">https://archive.ics.uci.edu/ml/datasets/Air+Quality</a>&nbsp;for more details.</p>\n\n<p>Relevant papers<br>\nS. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005.<br>\nSaverio De Vito, Marco Piga, Luca Martinotto, Girolamo Di Francia, CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization, Sensors and Actuators B: Chemical, Volume 143, Issue 1, 4 December 2009, Pages 182-191, ISSN 0925-4005.<br>\nS. De Vito, G. Fattoruso, M. Pardo, F. Tortorella and G. Di Francia, Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction, in IEEE Sensors Journal, vol. 12, no. 11, pp. 3215-3224, Nov. 2012. doi: 10.1109/JSEN.2012.2192425</p>\n\n<p><br>\nCitation request<br>\nS. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Monash University", 
      "@id": "https://orcid.org/0000-0001-8377-3241", 
      "@type": "Person", 
      "name": "Chang Wei Tan"
    }, 
    {
      "affiliation": "Monash University", 
      "@id": "https://orcid.org/0000-0002-3665-9021", 
      "@type": "Person", 
      "name": "Christoph Bergmeir"
    }, 
    {
      "affiliation": "Monash University", 
      "@id": "https://orcid.org/0000-0001-5334-3574", 
      "@type": "Person", 
      "name": "Francois Petitjean"
    }, 
    {
      "affiliation": "Monash University", 
      "@id": "https://orcid.org/0000-0001-9963-5169", 
      "@type": "Person", 
      "name": "Geoffrey I Webb"
    }
  ], 
  "url": "https://zenodo.org/record/3902673", 
  "datePublished": "2020-06-21", 
  "keywords": [
    "time series", 
    "regression"
  ], 
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  "identifier": "https://doi.org/10.5281/zenodo.3902673", 
  "@id": "https://doi.org/10.5281/zenodo.3902673", 
  "@type": "Dataset", 
  "name": "Benzene Concentration Dataset"
}
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