Outlier Detection and Missing Value in Time Series Ozone Data
K. Senthamarai Kannan, Manoj Kuppusamy, Arumugam Subbanna Gounder · International Journal of Scientific Research in Knowledge · 2015
Time series represents the collection of values obtained from sequential measurements over time. Time-series data mining stems from the desire to express our natural ability to visualize the shape of data. Humans rely on complex schemes in order to perform such tasks. A serious problem in analyzing Ozone hole data is what to do when missing or extreme values occur, perhaps as a result of a breakdown in automatic counting equipment. The objectives of this current work were to attempt to look at ways of solving this problem by using the residuals from a fitted ARIMA model is the most successful method at finding outliers and distinguishing them from other events, being less expensive than case deletion. The replacement values derived from the ARIMA model were found to be the most accurate.