TY - UNPB
T1 - Mining Seasonal Temporal Patterns in Time Series
AU - Ho Long, Van
AU - Ho, Nguyen Thi Thao
AU - Pedersen, Torben Bach
PY - 2022
Y1 - 2022
N2 - Very large time series are increasingly available from an ever wider range of IoT-enabled sensors, from which significant insights can be obtained through mining temporal patterns from them. A useful type of patterns found in many real-world applications exhibits periodic occurrences, and is thus called seasonal temporal pattern (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. This paper presents our Frequent Seasonal Temporal Pattern Mining from Time Series (FreqSTPfTS) solution providing: (1) The first solution for seasonal temporal pattern mining (STPM) from time series that can mine STP at different data granularities. (2) The STPM algorithm that uses efficient data structures and two pruning techniques to reduce the search space and speed up the mining process. (3) An approximate version of STPM that uses mutual information, a measure of data correlation, to prune unpromising time series from the search space. (4) An extensive experimental evaluation showing that STPM outperforms the baseline in runtime and memory consumption, and can scale to big datasets. The approximate STPM is up to an order of magnitude faster and less memory consuming than the baseline, while maintaining high accuracy.
AB - Very large time series are increasingly available from an ever wider range of IoT-enabled sensors, from which significant insights can be obtained through mining temporal patterns from them. A useful type of patterns found in many real-world applications exhibits periodic occurrences, and is thus called seasonal temporal pattern (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. This paper presents our Frequent Seasonal Temporal Pattern Mining from Time Series (FreqSTPfTS) solution providing: (1) The first solution for seasonal temporal pattern mining (STPM) from time series that can mine STP at different data granularities. (2) The STPM algorithm that uses efficient data structures and two pruning techniques to reduce the search space and speed up the mining process. (3) An approximate version of STPM that uses mutual information, a measure of data correlation, to prune unpromising time series from the search space. (4) An extensive experimental evaluation showing that STPM outperforms the baseline in runtime and memory consumption, and can scale to big datasets. The approximate STPM is up to an order of magnitude faster and less memory consuming than the baseline, while maintaining high accuracy.
U2 - 10.48550/arXiv.2206.14604
DO - 10.48550/arXiv.2206.14604
M3 - Preprint
BT - Mining Seasonal Temporal Patterns in Time Series
PB - arXiv
ER -