Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Neural Networks for Identification, Prediction, and Control
Neural Networks for Identification, Prediction, and Control
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Data Mining in Time Series Database
Data Mining in Time Series Database
Recognition of control chart patterns using improved selection of features
Computers and Industrial Engineering
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Time Series Discretization via MDL-Based Histogram Density Estimation
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
An approach to dimensionality reduction in time series
Information Sciences: an International Journal
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Control Chart Patterns (CCPs) can be considered as time series. Industry widely used them in their process control. Therefore, accurate classification of these CCPs is vital as abnormalities can then be detected at the earliest stage. This work proposes a framework for neural networks based classifier of CCPs. It adopts a symbolic representation technique known as Symbolic Aggregate ApproXimation (SAX) in preprocessing. It was discovered that difficulty in classifying CCPs with high signal to noise ratio lies in differentiating among three very similar categories within their six categories. Synergism of neural networks is used as the classifier. Classification comprises two levels, the super class and individual category levels. The recurrent neural network known as Time-lag network is selected as classifiers. The proposed method yields superior performance than any previous neural network based classifiers which used the Generalized Autoregressive Conditional Heteroskedasticity (GARH) Model to generate CCPs.