TY - GEN
T1 - Multivariate time series early classification using multi-domain deep neural network
AU - Huang, Huai Shuo
AU - Liu, Chien-Liang
AU - Tseng, Vincent S.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Early classification on multivariate time series is an important research topic in data mining with wide applications to various domains like medical diagnosis, motion detection and financial prediction, etc. Shapelet is probably one of the most commonly used approaches to tackle early classification problem, but one drawback of shaplet is its inefficiency. More importantly, the extracted shapelets may not be applicable to every test case at any time point. This work focuses on early classification of multivariate time series and proposes a novel framework named Multi-Domain Deep Neural Network (MDDNN), in which convolutional neural network (CNN) and long-short term memory (LSTM) are incorporated to learn feature representation and relationship embedding in the long sequences with long time lags. The proposed model can make predictions at any time point of a multivariate time series with the help of a truncation process. We conducted experiments on four real datasets and compared with state-of-the-art algorithms. The experimental results indicate that the proposed method outperforms the alternatives significantly on both of earliness and accuracy. Detailed analysis about the proposed model is also provided in this work. To the best of our knowledge, this is the first work that incorporates deep neural network methods (CNN and LSTM) and multi-domain approach to boost the problem of early classification on multivariate time series.
AB - Early classification on multivariate time series is an important research topic in data mining with wide applications to various domains like medical diagnosis, motion detection and financial prediction, etc. Shapelet is probably one of the most commonly used approaches to tackle early classification problem, but one drawback of shaplet is its inefficiency. More importantly, the extracted shapelets may not be applicable to every test case at any time point. This work focuses on early classification of multivariate time series and proposes a novel framework named Multi-Domain Deep Neural Network (MDDNN), in which convolutional neural network (CNN) and long-short term memory (LSTM) are incorporated to learn feature representation and relationship embedding in the long sequences with long time lags. The proposed model can make predictions at any time point of a multivariate time series with the help of a truncation process. We conducted experiments on four real datasets and compared with state-of-the-art algorithms. The experimental results indicate that the proposed method outperforms the alternatives significantly on both of earliness and accuracy. Detailed analysis about the proposed model is also provided in this work. To the best of our knowledge, this is the first work that incorporates deep neural network methods (CNN and LSTM) and multi-domain approach to boost the problem of early classification on multivariate time series.
KW - Convolutional Neural Networks
KW - Early Classification
KW - LSTM
KW - Multi-domain Inputs
KW - Time Series Analysis
UR - http://www.scopus.com/inward/record.url?scp=85062878628&partnerID=8YFLogxK
U2 - 10.1109/DSAA.2018.00019
DO - 10.1109/DSAA.2018.00019
M3 - Conference contribution
AN - SCOPUS:85062878628
T3 - Proceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018
SP - 90
EP - 98
BT - Proceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018
A2 - Bonchi, Francesco
A2 - Provost, Foster
A2 - Eliassi-Rad, Tina
A2 - Wang, Wei
A2 - Cattuto, Ciro
A2 - Ghani, Rayid
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018
Y2 - 1 October 2018 through 4 October 2018
ER -