Fundamentals of speech recognition
Fundamentals of speech recognition
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
Similarity Search Over Time-Series Data Using Wavelets
ICDE '02 Proceedings of the 18th 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
Exact indexing of dynamic time warping
Knowledge and Information Systems
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on 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
An Improvement of PAA for Dimensionality Reduction in Large Time Series Databases
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Hi-index | 0.00 |
Piecewise Aggregate Approximation (PAA) is a very simple dimensionality reduction method for time series mining. It minimizes dimensionality by the mean values of equal sized frames, which misses some important information and sometimes causes inaccurate results in time series mining. In this paper, we propose an improved PAA, which is based on statistical features including a mean-based feature and variance-based feature. We propose two versions of the improved PAA which have the same preciseness except for the different CPU time cost. Meanwhile, we also provide theoretical analysis for their feasibility and prove that our method guarantees no false dismissals. Experimental results demonstrate that the improved PAA has better tightness of lower bound and more powerful pruning ability.