Temporal modeling using Granger neural encoding = 그레인저 신경망 인코딩을 활용한 시간적 모델링

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Temporal modeling refers to analyzing the causal relationship between given time series data and modeling the predictive function based on this, and it is widely used in various applications such as weather forecasting, market price prediction or anomaly detection. Generally, in the case of causality analysis, Granger causality has been used for a long time, but it becomes computationally intractable when it uses a lot of past information called delay terms. In this thesis, to solve the problem of previous Granger causality, we propose Granger neural encoding which consists of non-linear encoding and lasso Granger model. In non-linear encoding, we use deep learning models such as CNN (convolutional neural networks) and RNN-LSTM (recurrent neural network - long short-term memory) which have state-of-the-art performance in various applications. Then we analyze the causality on those encoded time series data by using lasso Granger model. In addition to Granger neural encoding, we propose a two-staged dynamic capacity network, each consisting of a simple Granger neural encoding model for causal analysis and a complex one for precise prediction based on them. In the end of this thesis, we verify dynamic capacity Granger neural encoding has improved modeling performance than other comparative temporal models.
Advisors
Shin, Jinwooresearcher신진우researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2017
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2017.2,[iii, 32 p. :]

Keywords

Temporal modeling; Granger causality; deep learning; Granger neural encoding; dynamic capacity network; 시간적 모델링; 그레인저 인과관계; 딥 러닝; 그레인저 신경망 인코딩; 동적 용량 신경망 모델

URI
http://hdl.handle.net/10203/243330
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675438&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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