Mining Seasonal Temporal Patterns in Time Series

As IoT-enabled sensors become more pervasive, very large time series data are increasingly generated and made available for advanced data analytics. By mining temporal patterns from the available data, valuable insights can be extracted to support decision making. A useful type of patterns found in many real-world applications exhibits periodic occurrences, and is thus called seasonal temporal patterns (STP). Compared to regular patterns, mining seasonal temporal patterns is more challenging since traditional measures such as support and confidence do not capture the seasonality characteristics. Further, the anti-monotonicity property does not hold for STPs, and thus, resulting in an exponential search space. We propose a first solution for seasonal temporal pattern mining (STPM) from time series that can mine STP at different data granularities. We design efficient data structures and use two pruning techniques for the STPM algorithm that downsize the search space and accelerate the mining process. Further, based on the mutual information measure, we propose an approximate version of STPM that only mine seasonal patterns on the promising time series. Finally, extensive experiments with real-world and synthetic datasets show that STPM outperforms the baseline in terms of runtime and memory usage, and can scale to large datasets. The approximate STPM is up to an order of magnitude faster and less memory-consuming than the baseline, while maintaining high accuracy.