A prior-free encode-decode change detection test to inspect datastreams for concept drift

Online change detection in datastreams has attracted many researchers and is becoming a very hot topic whose relevance will further increase with research on Big Data. Concept drift is induced by changes in stationarity of the process generating the data caused by faults, time variance of the environment and inaccuracy of the change detection mechanism. Here, we propose a recurrent auto-associative Encode-Decode machine trained to reconstruct input data. The generated residual is then inspected for structural changes with a Change Detection Test (CDT). Although any CDT can be used, in the paper we focus the attention on the Hierarchical Intersection of Confidence Intervals change detection test for its capability of controlling false positives with a two layered test and an online version of the Lepage Change Point Model. Once concept drift is detected, the designed Encode-Decode machine, globally acting as an Encode-Decode CDT, is retrained on new data to detect subsequent changes.