In this paper, the complexity of detecting an outlier has been shown. The importance of an outlier has been presented with the need to interpret these outliers. The sensors collect data with certain sampling time period and these data are stored which contribute to the huge database. These sensors can be electrocardiogram sensor which monitors electrical and muscular functions of the heart, they can be pollution monitoring stations at the airports, they can be heat sensors in a building, and they can be flight data recorders (FDR) and so on. Sometimes, these sensors miss to detect the signal due to some technical fault and hence the output is “Not Available (NA)”. These NA time stamps create unnecessary problems which lead to unwanted outputs when the data is processed. In this paper, an algorithm is presented which replaces these NA values with most probable values. When the data is ready with all NA values removed, the data is processed for detecting the outlier. In this paper, an outlier is being detected by analyzing the signal in the frequency domain along with the mean in the time domain. A large data set is divided into equal sized blocks. Each block is then converted to its frequency domain and mean is calculated in the time domain. These two parameters are considered to detect any outlier in the block. This approach removes the complexity in the algorithm without compromising in the efficiency of detecting an outlier. Hence, a large database of values is processed in relatively less time with appreciable accuracy. © 2018, Springer International Publishing AG.
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S. Suman and Rajathilagam, B., “Outlier detection in time-series data: Specific to nearly uniform signals from the sensors”, Lecture Notes in Computational Vision and Biomechanics, vol. 28, pp. 697-704, 2018.