Abstract

Discovery of non-obvious relationships between time series is an important problem in many domains, such as financial, sensory, and scientific data analysis. We consider data mining in aligned time series, which arise, e.g., in numerous online monitoring applications, and we are interested in finding time series that reflect the same external events. The time series can have different vertical positions, scales and overall trends, but still show related features at the same locations. The features can be short term such as small peaks and turns, or long term such as wider mountains and valleys. We propose using a wavelet transformation of a time series to produce a natural set of features for the sequence. Wavelet transformation yields features that describe properties of the sequence both at various locations and at varying time granularities. In the proposed method, these features are processed so that they are insensitive to changes in the vertical position, scaling, and overall tre...

Citations