International Journal of Computer Applications
Time series data streams are common in wireless sensor networks in nowadays. This type of data is having uncertainty due to the limitation of the measuring equipments or other sources of corrupting noise, leading to uncertain data. As uncertain streaming data is continuously generated, mining algorithms should be able to analyze the uncertain data. To detect the outliers in this paper, the authors propose two continuous distance-based outlier detection approaches (an exact and an approximate) are proposed for uncertain time series data streams. These two algorithms are implemented based on the cell based approach.