Nonlinear analysis of data sampled nonuniformly in time
Conference proceedings on Interpretation of time series from nonlinear mechanical systems
Journal of the ACM (JACM)
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Infominer: mining surprising periodic patterns
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding Maximal Repetitions in a Word in Linear Time
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
InfoMiner+: Mining Partial Periodic Patterns with Gap Penalties
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Constructing Suffix Tree for Gigabyte Sequences with Megabyte Memory
IEEE Transactions on Knowledge and Data Engineering
SMCA: A General Model for Mining Asynchronous Periodic Patterns in Temporal Databases
IEEE Transactions on Knowledge and Data Engineering
Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
Practical methods for constructing suffix trees
The VLDB Journal — The International Journal on Very Large Data Bases
WARP: Time Warping for Periodicity Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining Dense Periodic Patterns in Time Series Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Adaptive, hands-off stream mining
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Spectral analysis of nonuniformly sampled data -- a review
Digital Signal Processing
Adapting machine learning technique for periodicity detection in nucleosomal locations in sequences
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Efficient Periodicity Mining in Time Series Databases Using Suffix Trees
IEEE Transactions on Knowledge and Data Engineering
| Hi-index | 0.00 |
Periodic pattern detection is an important data mining task that highlights the temporal regularities within the data. It aims at finding if a partial or full pattern has a cyclic repetition in the considered time series or data sequence. Periodicity is found in large number of datasets including meteorological data, transaction count, computer network traffic, power consumption, sunspots, Electrocardiography ECG, biological sequences such as DNA and protein [33]. Periodic pattern analysis not only helps in understanding the behavior of the data but also contributes in predicting the future trends of the data. There are several algorithms reported in the literature for periodicity detection in time series and biological sequences [3,34] but none of these algorithms discuss the non-uniformly sampled data. General assumption in the time series and sequence data is that the consecutive data values are sampled at regular or uniform interval of time. But this assumption hardly holds in real datasets; for example the stock market data analyzed in this paper record various features for each working day. This data has a quite a few missing values for weekly and arbitrary holidays. Although handling this issue is not very complex but requires careful handling. In this paper we analyze the stock market data in detail and show how the periodic pattern analysis may provide the understanding of the data to predict the future trends. Our experimental results show that consideration of missing values in stock market data results in much larger number of interesting results than the trivial periodicity detection approach ignoring the missing values.