Time series classification using deep learningTime series classification using deep learning

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.


Consensus clustering of time series data
Yetere Kurşun, Ayça; Batmaz, İnci; İyigün, Cem; Department of Scientific Computing (2014)
In this study, we aim to develop a methodology that merges Dynamic Time Warping (DTW) and consensus clustering in a single algorithm. Mostly used time series distance measures require data to be of the same length and measure the distance between time series data mostly depends on the similarity of each coinciding data pair in time. DTW is a relatively new measure used to compare two time dependent sequences which may be out of phase or may not have the same lengths or frequencies. DTW aligns two time serie...
Ozogur-Akyuz, S.; Weber, Gerhard Wilhelm (2009-06-03)
In Machine Learning (ML) algorithms, one of the crucial issues is the representation of the data. As the data become heterogeneous and large-scale, single kernel methods become insufficient to classify nonlinear data. The finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, we propose a novel method of "infinite" kernel combinations for learning problems with the help of infinite and semi-infinite programming regarding all elements in kernel space. Looking...
Effective gene expression data generation framework based on multi-model approach
Sirin, Utku; Erdogdu, Utku; Polat, Faruk; TAN, MEHMET; Alhajj, Reda (Elsevier BV, 2016-06-01)
Objective: Overcome the lack of enough samples in gene expression data sets having thousands of genes but a small number of samples challenging the computational methods using them.
LinGraph: a graph-based automated planner for concurrent task planning based on linear logic
Kortik, Sitar; Saranlı, Uluç (Springer Science and Business Media LLC, 2017-10-01)
In this paper, we introduce an automated planner for deterministic, concurrent domains, formulated as a graph-based theorem prover for a propositional fragment of intuitionistic linear logic, relying on the previously established connection between intuitionistic linear logic and planning problems. The new graph-based theorem prover we introduce improves planning performance by reducing proof permutations that are irrelevant to planning problems particularly in the presence of large numbers of objects and a...
Data mining in deductive databases using query flocks
Toroslu, İsmail Hakkı (Elsevier BV, 2005-04-01)
Data mining can be defined as a process for finding trends and patterns in large data. An important technique for extracting useful information, such as regularities, from usually historical data, is called as association rule mining. Most research on data mining is concentrated on traditional relational data model. On the other hand, the query flocks technique, which extends the concept of association rule mining with a 'generate-and-test' model for different kind of patterns, can also be applied to deduct...
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.