Time-series data is one of the most common forms of data encountered in a wide variety of scenarios such as the stock markets, sensor data, fault monitoring, machine state monitoring, environmental applications, or medical data. The problem of clustering finds numerous applications in the time-series domain, such as the determination of groups of entities with similar trends. Time-series clustering has numerous applications in diverse problem domains:
Financial Markets: In financial markets, the values of the stocks represent time series which continually vary with time. The clustering of such time series can provide numerous insights into the trends in the underlying data.
Medical Data: Different kinds of medical data such as EEG readings are in the form of time series. The clustering of such time-series can provide an understanding of the common shapes in the data. These common shapes can be related to different kinds of diseases.
Earth Science Applications: Numerous applications in earth science, such as temperature or pressure, correspond to series, which can be mined in order to determine the frequent trends in the data. These can provide an idea of the common climactic trends in the data.
Machine State Monitoring: Numerous forms of machines create sensor data, which provides a continuous idea of the states of these objects. These can be used in order to provide an idea of the underlying trends.
Spatio-temporal Data: Trajectory data can be considered a form of multivariate time-series data, in which the X-coordinates and Y-coordinates of objects correspond to continuously varying series. The trends in these series can be used in order to determine the important trajectory clusters in the data.