Matching and Retrieving Sequential Patterns Under Regression

Sequential pattern matching and retrieving is of real value. For example, finding stocks in the NASDAQ market whose closing prices are always about $β₀ higher than or β₁ times as that of a given company. The probelm reduces to linear pattern retrieval: given query X, find all sequence Y from database S so that Y = β₀ + β₁ with confidence C. In this paper, we novelly introduce SLR (Simple Linear Regression) model [5,7] to solve this problem. We extend 1-dimensional R^2 to ER^2 for multi-dimensional sequence matching, such as on-line handwritten signature. In addition, we develop SLR+FFT pruning techniques based on SLR to speed up retrieval without incurring any false dismissal. Experimental results show that the pruning ratio of SLR+FFT is efficient (can be above 99%). Experiments on real stocks discovered many interesting patterns. Preliminary test on on-line signature recognition using ER^2 as similarity measure also shows high accuracy.