Context I get this dataset from UCI Machine Learning. I very interested with this dataset because one of our global warming problem is about air quality in some big city very serious. In UCI ML get this data from sensor device that located in Italy. Also you can read about the dataset in the description. Content I get this data from UCI Machine Learning. Here is about descripstion rows and column also another description. "The dataset contains 9358 instances of hourly averaged responses from an array of 5 metal oxide chemical sensors embedded in an Air Quality Chemical Multisensor Device. The device was located on the field in a significantly polluted area, at road level,within an Italian city. 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. 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. Evidences of cross-sensitivities as well as both concept and sensor drifts are present as described in De Vito et al., Sens. And Act. B, Vol. 129,2,2008 (citation required) eventually affecting sensors concentration estimation capabilities. Missing values are tagged with -200 value. " Acknowledgements Thank to UCI [https://archive.ics.uci.edu/ml/index.php][1] Inspiration I would like to see another method to classify or cluster this dataset with timeseries purpose. [1]: https://archive.ics.uci.edu/ml/index.php