| Cosma Rohilla Shalizi Santa Fe Institute 1399 Hyde Park Rd. Santa Fe, NM 87501, USA and Center for the Study of Complex Systems University of Michigan Ann Arbor, MI 48109, USA |
Kristina Lisa Shalizi Santa Fe Institute 1399 Hyde Park Rd. Santa Fe, NM 87501, USA and Physics Department University of San Francisco 2130 Fulton Street San Francisco, CA 94117, USA |
James P. Crutchfield Santa Fe Institute 1399 Hyde Park Rd. Santa Fe, NM 87501, USA |
ABSTRACT: We present a new algorithm for discovering patterns in time series or other sequential data. In the prior companion work, Part I, we reviewed the underlying theory, detailed the algorithm, and established its asymptotic reliability and various estimates of its data-size asymptotic rate of convergence. Here, in Part II, we outline the algorithm's implementation, illustrate its behavior and ability to discover even "difficult" patterns, demonstrate its superiority over alternative algorithms, and discuss its possible applications in the natural sciences and to data mining.