Motif-based Rule Discovery for Predicting Real-valued Time Series
  Time series prediction is of great significance in many applications and has
attracted extensive attention from the data mining community. Existing work
suggests that for many problems, the shape in the current time series may
correlate an upcoming shape in the same or another series. Therefore, it is a
promising strategy to associate two recurring patterns as a rule's antecedent
and consequent: the occurrence of the antecedent can foretell the occurrence of
the consequent, and the learned shape of consequent will give accurate
predictions. Earlier work employs symbolization methods, but the symbolized
representation maintains too little information of the original series to mine
valid rules. The state-of-the-art work, though directly manipulating the
series, fails to segment the series precisely for seeking
antecedents/consequents, resulting in inaccurate rules in common scenarios. In
this paper, we propose a novel motif-based rule discovery method, which
utilizes motif discovery to accurately extract frequently occurring consecutive
subsequences, i.e. motifs, as antecedents/consequents. It then investigates the
underlying relationships between motifs by matching motifs as rule candidates
and ranking them based on the similarities. Experimental results on real open
datasets show that the proposed approach outperforms the baseline method by
23.9%. Furthermore, it extends the applicability from single time series to
multiple ones.
