Extended Vertical Lists for Temporal Pattern Mining from Multivariate Time Series
This work addresses the computational efficiency challenge in temporal pattern mining for researchers and practitioners in time series analysis, but it is incremental as it builds on prior methods with a trade-off in memory.
The authors tackled the problem of mining predictive complex temporal patterns from multivariate time series by developing a new method called Fast Temporal Pattern Mining with Extended Vertical Lists, which uses a novel data structure to track pattern positions. The result is a significant speed-up in performance compared to the previous algorithm, though at the cost of increased memory usage.
Temporal Pattern Mining (TPM) is the problem of mining predictive complex temporal patterns from multivariate time series in a supervised setting. We develop a new method called the Fast Temporal Pattern Mining with Extended Vertical Lists. This method utilizes an extension of the Apriori property which requires a more complex pattern to appear within records only at places where all of its subpatterns are detected as well. The approach is based on a novel data structure called the Extended Vertical List that tracks positions of the first state of the pattern inside records. Extensive computational results indicate that the new method performs significantly faster than the previous version of the algorithm for TMP. However, the speed-up comes at the expense of memory usage.