An Improvement of PAA on Trend-Based Approximation for Time Series
This work addresses a specific limitation in time series dimension reduction for data mining applications, representing an incremental improvement over existing methods.
The authors tackled the problem of missing trend information in Piecewise Aggregate Approximation (PAA) for time series mining by proposing two new methods that incorporate approximate trend features, resulting in improved accuracy and effectiveness in similarity measurement for classification and anomaly detection.
Piecewise Aggregate Approximation (PAA) is a competitive basic dimension reduction method for high-dimensional time series mining. When deployed, however, the limitations are obvious that some important information will be missed, especially the trend. In this paper, we propose two new approaches for time series that utilize approximate trend feature information. Our first method is based on relative mean value of each segment to record the trend, which divide each segment into two parts and use the numerical average respectively to represent the trend. We proved that this method satisfies lower bound which guarantee no false dismissals. Our second method uses a binary string to record the trend which is also relative to mean in each segment. Our methods are applied on similarity measurement in classification and anomaly detection, the experimental results show the improvement of accuracy and effectiveness by extracting the trend feature suitably.