Online annotation and prediction for regime switching data streams
Proceedings of the 2009 ACM symposium on Applied Computing
Periodic Pattern Analysis in Time Series Databases
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Finding Structural Similarity in Time Series Data Using Bag-of-Patterns Representation
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Similarity search in multimedia time series data using amplitude-level features
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
ACM Computing Surveys (CSUR)
Rotation-invariant similarity in time series using bag-of-patterns representation
Journal of Intelligent Information Systems
Time series analysis of collaborative activities
CRIWG'12 Proceedings of the 18th international conference on Collaboration and Technology
Hybrid-Learning based data gathering in wireless sensor networks
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Long-memory time series ensembles for concept shift detection
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Compression in wireless sensor networks: A survey and comparative evaluation
ACM Transactions on Sensor Networks (TOSN)
Unsupervised categorization of human motion sequences
Intelligent Data Analysis
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For a given time series observation sequence, we can esti- mate the parameters of the AutoRegression Moving Average (ARMA) model, thereby representing a potentially long time series by a limited dimensional vector. In many applications, these parameter vectors will be separable into different groups, due to the diff- erent underlying mechanisms that generate differing time series. We can then use classification algorithms to predict the class of a new, uncategorized time series. For the purposes of a highly autonomous system, our approach to this classification uses memory -based learning and intensive cross-validation for feature and kernel selection. In an example application, we distinguish between driving data of a skilled, sober driver vs. a drunk driver, by calculating the ARMA model for the respective time series. In this paper, we first give a brief introduction to the theory of time series. We then discuss in detail our approach to time series recognition, using the ARMA model, and finish with experimental results.