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Error Detection of Ocean Depth Series Data with Area Partitioning and Using Sliding Window

In the ocean around the world, depth series ocean data of temperature and salinity have being measured. However, it is difficult to discriminate the errors from the normal data since the variation of ocean areas are different. In this research, using hierarchical clustering, we partitioned the ocean into some areas so that the ocean data have the same variation in each area. Then, transforming the ocean data into sets of sliding windows in consideration of depth series, we applied some anomaly detection methodologies. Finally, we succeeded in assigning high anomaly scores on errors that seemed to be normal.