Exploring the diverse world of SAX-based methodologies
Abstract Symbolic Aggregate Approximation (SAX) is a widely used method for time series
data analysis, known for its ability to transform continuous data to discrete symbols. While …
data analysis, known for its ability to transform continuous data to discrete symbols. While …
Distance-and Momentum-Based Symbolic Aggregate Approximation for Highly Imbalanced Classification
Time-series representation is the most important task in time-series analysis. One of the
most widely employed time-series representation method is symbolic aggregate …
most widely employed time-series representation method is symbolic aggregate …
Feature representation and similarity measure based on covariance sequence for multivariate time series
H Li, C Lin, X Wan, Z Li - IEEE Access, 2019 - ieeexplore.ieee.org
The high dimension of multivariate time series (MTS) is one of the major factors that impact
on the efficiency and effectiveness of data mining. It has two kinds of dimensions, time …
on the efficiency and effectiveness of data mining. It has two kinds of dimensions, time …
Slopewise Aggregate Approximation SAX: kee** the trend of a time series
In this work, we introduce the Slopewise Aggregate Approximation (SAA), an innovative
variation of the Piecewise Aggregate Approximation. The Slopewise Aggregate …
variation of the Piecewise Aggregate Approximation. The Slopewise Aggregate …