Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Anomaly Detection And Classification In Time Series With Kervolutional
  Neural Networks   Recently, with the development of deep learning, end-to-end neural network
architectures have been increasingly applied to condition monitoring signals.
They have demonstrated superior performance for fault detection and
classification, in particular using convolutional neural networks. Even more
recently, an extension of the concept of convolution to the concept of
kervolution has been proposed with some promising results in image
classification tasks. In this paper, we explore the potential of kervolutional
neural networks applied to time series data. We demonstrate that using a
mixture of convolutional and kervolutional layers improves the model
performance. The mixed model is first applied to a classification task in time
series, as a benchmark dataset. Subsequently, the proposed mixed architecture
is used to detect anomalies in time series data recorded by accelerometers on
helicopters. We propose a residual-based anomaly detection approach using a
temporal auto-encoder. We demonstrate that mixing kervolutional with
convolutional layers in the encoder is more sensitive to variations in the
input data and is able to detect anomalous time series in a better way.

34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph