Deep Temporal Convolution Network for Time Series Classification

Abstract

A neural network that matches with a complex data function is likely to boost the classification performance as it is able to learn the useful aspect of the highly varying data. In this work, the temporal context of the time series data is chosen as the useful aspect of the data that is passed through the network for learning. By exploiting the compositional locality of the time series data at each level of the network, shift-invariant features can be extracted layer by layer at different time scales. The temporal context is made available to the deeper layers of the network by a set of data processing operations based on the concatenation operation. A matching learning algorithm for the revised network is described in this paper. It uses gradient routing in the backpropagation path. The framework as proposed in this work attains better generalization without overfitting the network to the data, as the weights can be pretrained appropriately. It can be used end-to-end with multivariate time series data in their raw form, without the need for manual feature crafting or data transformation. Data experiments with electroencephalogram signals and human activity signals show that with the right amount of concatenation in the deeper layers of the proposed network, it can improve the performance in signal classification.

Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

“…In addition, the Adaboost-BP neural network has defects such as long training time and difficulty in determining the number of weak learners. The Deep Temporal Convolution Network (DTCN) has a higher efficiency in training samples and data classification [ 42 ]. Therefore, combining deep learning technology and multiple data fusion technology to identify the hardness of coal and rock will be interesting work for our future studies.…”

Identifying Prenatal and Postnatal Determinants of Infant Growth: A Structural Equation Modelling Based Cohort Analysis

“…Some machine learning methods have emerged to identify the causal features from the large dataset [ 41 , 42 ]. These could be a new way forward to analyse the current dataset for future work.…”

A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization

“…Targeting moving sound sources is mostly considered with direction or angle measurements [46,47], combined with microphone arrays [48], or mostly relying on the measurement of propagation time [49], given the achievement of very satisfactory accuracy, despite the complexity of the physical devices employed. While in the space-state domain Kalman filters and maximum a posteriori estimation are commonly used [50], ANNs have also been considered [51,52]. In this case, time series prediction is performed with the resource of recurrent neural networks composed of long short-term memory cells [53].…”