Corpus ID: 16409971

Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks

@inproceedings{Wang2014EncodingTS,
  title={Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks},
  author={Zhiguang Wang and Tim Oates},
  year={2014}
}
Inspired by recent successes of deep learning in computer vision and speech recognition, we propose a novel framework to encode time series data as different types of images, namely, Gramian Angular Fields (GAF) and Markov Transition Fields (MTF). This enables the use of techniques from computer vision for classification. Using a polar coordinate system, GAF images are represented as a Gramian matrix where each element is the trigonometric sum (i.e., superposition of directions) between… Expand
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