Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Improvement of PAA on Trend-Based Approximation for Time Series   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.

19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys