Abstract
Association rule mining that mainly focuses on symbolic items presented in transactions has attracted considerable interest since a rule provides a concise and intuitive description of knowledge. However, a time series is a sequence of data that is typically recorded in temporal order at fixed intervals of time. In order to mining rules in the context of time series data, a symbolic aggregate approximation (SAX) representation that could discretize the real-valued and high-dimensional time series data into segments and convert each segment to a symbol is applied in this paper. On this basis, a modified CBA algorithm is proposed to discover Class Sequential Rules (CSRs) and make the final prediction at first. Then we propose a new lazy associative classification method, in which the computation is performed on a demand driven basis. This is in contrast to rule-based classification methods like CBA which generate excessive number of rules, but is still unable to cover some test data with the discovered rules. Various experimental results show that our lazy associative classification for time series can be interpretable and competitive with the current state-of-the-art algorithm. In addition, four different methods that select the mined CSR(s) are proposed for carrying out associative classification.