Redundancy in spatial databases
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
External memory algorithms and data structures: dealing with massive data
ACM Computing Surveys (CSUR)
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Cyclically Repeated Patterns
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
SMCA: A General Model for Mining Asynchronous Periodic Patterns in Temporal Databases
IEEE Transactions on Knowledge and Data Engineering
Mining for weak periodic signals in time series databases
Intelligent Data Analysis
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
A method for discovering components of human rituals from streams of sensor data
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A review on time series data mining
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
The search for weak periodic signals in time series data is an active topic of research. Given the fact that rarely a real world dataset is perfectly periodic, this paper approaches this problem in terms of data mining, trying to discover weak periodic signals in time series databases, when no period length is known in advance. In existing time series mining algorithms, the period length is user-specified. We propose an algorithm for finding approximate periodicities in large time series data, utilizing autocorrelation function and FFT. This algorithm is an extension to the partial periodicity detection algorithm presented in a previous paper of ours. We provide some mathematical background as well as experimental results.