Matching and retrieving sequential patterns using regression

Sequential pattern mining can prove to be very useful for predicating future activities, interpreting recurring phenomena, extracting similarities in a series of events, etc. For example, in the NASDAQ market, the problem of finding stocks whose closing prices are always about $ β0 higher than or β1 times the stocks of a given company, reduces to linear pattern retrieval: given query X , find all sequences Y from the database S so that, Y = β0 + β1X with confidence C. In this paper, we introduce a novel approach using the Simple Linear Regression (SLR) model to match and retrieve sequential patterns. We extend the one-dimensional R model to ER for multi-dimensional sequence matching. In addition, we present the SLR+FFT pruning technique to speed up data retrieval without incurring any false dismissal. Experimental results on both synthetic and real datasets show that the pruning ratio of SLR+FFT can be above 99%. Applying the retrieval technique to real stocks resulted in the discovery many interesting patterns, some of which are presented in the paper. Also, using ER as the similarity measure for on-line signature recognition yielded high accuracy.