Time series classification using deep learningTime series classification using deep learning

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2016
Hatipoğlu, Poyraz Umut
Deep learning is a fast-growing and interesting field due to the need to represent statistical data in a more complex and abstract way. Development in the processors and graphics processing unit technology effects undeniably that the deep networks get that popularity. The main purpose of this work is to develop robust and full functional time series classification method. To achieve this intent a deep learning based novel methods are proposed. Because time series data can have complex and variable structure, it may be more suitable to use algorithms that can handle the nonlinear sophisticated operations rather than shallow-structured methods. While shallow structured methods need handcrafted features and expert knowledge about data, deep learning based algorithms are capable of working with raw features. Both deep belief network and stacked autoencoders based architectures are constructed and trained for the dataset gathered from different researches areas. In time series classification, even though dynamic time warping and nearest neighbor based methods are hard to beat, many classification methods have been studied recently. To examine the performance of proposed method comparative analysis is conducted with popular benchmark methods. Despite higher accuracy in the results, the deep learning based methods cannot outperform superiorly.

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Citation Formats
P. U. Hatipoğlu, “Time series classification using deep learningTime series classification using deep learning,” M.S. - Master of Science, Middle East Technical University, 2016.