Time Series Discretization via MDL-Based Histogram Density Estimation

In knowledge discovery from real-valued time series, discretization is often a key preprocessing that extends the applicability of sophisticated tools for symbolic data mining or logic-based machine learning. For finding meaningful discrete values that can be directly translated into some intuitive symbols, this paper proposes a novel discretization method based on density estimation using a two-dimensional (measurement vs. time) histogram of variable-width bins. We extend Kontkanen and Myllymaki´s histogram construction method into our two dimensional case, keeping the efficiency brought by dynamic programming. Experimental results with artificial and real datasets show the robustness and the usefulness of the proposed method.