Adaptive query processing for time-series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Using Signature Files for Querying Time-Series Data
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
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
Clustering time series from ARMA models with clipped data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Multiresolution Symbolic Representation of Time Series
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Optimizing time series discretization for knowledge discovery
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
New Time Series Data Representation ESAX for Financial Applications
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Perception-based approach to time series data mining
Applied Soft Computing
Modelling Medical Time Series Using Grammar-Guided Genetic Programming
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Proceedings of the VLDB Endowment
Two Novel Adaptive Symbolic Representations for Similarity Search in Time Series Databases
APWEB '10 Proceedings of the 2010 12th International Asia-Pacific Web Conference
A review on time series data mining
Engineering Applications of Artificial Intelligence
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Expert Systems with Applications: An International Journal
Using derivatives in time series classification
Data Mining and Knowledge Discovery
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In recent years many studies have been proposed for knowledge discovery in time series. Most methods use some technique to transform raw data into another representation. Symbolic representations approaches have shown effectiveness in speedup processing and noise removal. The current most commonly used algorithm is the Symbolic Aggregate Approximation (SAX). However, SAX doesn't preserve the slope information of the time series segments because it uses only the Piecewise Aggregate Approximation for dimensionality reduction. In this paper, we present a symbolic representation method to dimensionality reduction and discretization that preserves the behavior of slope characteristics of the time series segments. The proposed method was compared with the SAX algorithm using artificial and real datasets with 1-nearest-neighbor classification. Experimental results demonstrate the method effectiveness to reduce the error rates of time series classification and to keep the slope information in the symbolic representation.