Dataset shift is a major challenge in the non-stationary environments wherein the input data distribution may change over time. Detecting the dataset shift point in the time-series data, where the distribution of time-series changes its properties, is of utmost interest. Dataset shift exists in a broad range of real-world systems. In such systems, there is a need for continuous monitoring of the process behavior and tracking the state of the shift so as to decide about initiating adaptive corrections in a timely manner. This paper presents an algorithm to detect the shift-point in a non-stationary time-series data. The proposed method detects the shift-point based on an exponentially weighted moving average (EWMA) control chart for auto-correlated observations. This algorithm is suitable to be run in real-time and monitors the data to detect the dataset shift. Its performance is evaluated through experiments using synthetic and real-world datasets. Results show that all the dataset-shifts are detected without the delay.
|Title of host publication||Unknown Host Publication|
|Number of pages||6|
|Publication status||Published - 13 Oct 2013|
|Event||IEEE International Conference on Systems, Man, and Cybernetics - Manchester, UK.|
Duration: 13 Oct 2013 → …
|Conference||IEEE International Conference on Systems, Man, and Cybernetics|
|Period||13/10/13 → …|