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Improving Time Series Classification Algorithms Using Octave-Convolutional Layers
Samuel Harford, Fazle Karim, Houshang Darabi
Deep learning models utilizing convolution layers have achieved state-of-the-art performance on univariate time series classification tasks. In this work, we propose improving CNN…
Adversarial Attacks on Multivariate Time Series
Samuel Harford, Fazle Karim, Houshang Darabi
Classification models for the multivariate time series have gained significant importance in the research community, but not much research has been done on generating adversarial s…
Adversarial Attacks on Time Series
Fazle Karim, Somshubra Majumdar, Houshang Darabi
Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples f…
Insights into LSTM Fully Convolutional Networks for Time Series Classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi
Long Short Term Memory Fully Convolutional Neural Networks (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN) have shown to achieve state-of-the-art performance on the task of classifyi…
Multivariate LSTM-FCNs for Time Series Classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi +1
Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the L…
LSTM Fully Convolutional Networks for Time Series Classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi +1
Fully convolutional neural networks (FCN) have been shown to achieve state-of-the-art performance on the task of classifying time series sequences. We propose the augmentation of f…