Demonstration of an application for remote outlier detection and accommodation

This work describes a tool for data analysis and time series cleaning, with a specific focus on outlier detection and accommodation. Data can be previously collected or obtained in real-time from a remote laboratory experiment using a wireless sensor network ({WSN}). The tool, presented as a local Matlab Graphical User Interface or as a remote application, allows the user to fill in missing data, resample and select a wide range of state-of-the-art and classical outlier detection and accommodation methods such as Kernel {PCA}, Modified Z-Score and Grubb's Test. After the accommodation, the different methods can be compared one with another, as well as with the original series, providing several scalar metrics, such as Euclidian Distance or Complex-Invariant Time Distance, and graphical comparison metrics. Thus, this tool will provide quick access to time series cleaning techniques, applicable, in particular, to data from industrial or clinical scenarios.