Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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
Finding similarity in time series data by method of time weighted moments
ADC '05 Proceedings of the 16th Australasian database conference - Volume 39
New Time Series Data Representation ESAX for Financial Applications
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Ranked subsequence matching in time-series databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
An improved piecewise aggregate approximation based on statistical features for time series mining
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Piecewise cloud approximation for time series mining
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
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Many dimensionality reduction techniques have been proposed for effective representation of time series data. Piecewise Aggregate Approximation (PAA) is one of the most popular methods for time series dimensionality reduction. While PAA approach allows a very good dimensionality reduction, PAA minimizes dimensionality by the mean values of equal sized frames. This mean value based representation may cause a high possibility to miss some important patterns in some time series datasets. In this work, we propose a new approach based on PAA, which we call Piecewise Linear Aggregate Approximation (PLAA). PLAA is the combination of a mean-based and a slope-based dimensionality reduction. We show that PLAA can improve representation preciseness through a better tightness of lower bound in comparison to PAA.