Data Preprocessing Techniques for Handling Time Series data for Environmental Science Studies

Data Preprocessing Techniques for Handling Time Series data for Environmental Science Studies

© 2021 by IJETT Journal
Volume-69 Issue-5
Year of Publication : 2021
Authors : Ebin Antony, N S Sreekanth, R K Sunil Kumar, Nishanth T
DOI :  10.14445/22315381/IJETT-V69I5P227

How to Cite?

Ebin Antony, N S Sreekanth, R K Sunil Kumar, Nishanth T, "Data Preprocessing Techniques for Handling Time Series data for Environmental Science Studies," International Journal of Engineering Trends and Technology, vol. 69, no. 5, pp. 196-207, 2021. Crossref,

The present article discusses various preprocessing techniques suitable for dealing with time series data for environmental science-related studies. The errors or noises due to electronic sensor fault, fault in the communication channel, etc., are considered here. Such errors or glitches that occur during the data acquisition or transmission phases need to be eliminated before it fed to the forecasting or classification systems. Computationally simple and efficient techniques are discussed here so that they can even be adopted for a hard real-time system environment. While adopting these techniques, we may also end up with some of the real genuine values, which may consider as an outlier. A special indicator function, the moving Inter Quartile Range (MIQR) algorithm, is proposed to overcome such special cases.

Time Series Analysis, Data Preprocessing, Moving Inter Quartile Range, Environmental Science, Data Science

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