Adaptive Dila-DenseNet for Image based Time Series Classification in IoT

Abstract

As a critical problem in time series data mining, time series classification (TSC) has always been a challenging task for high-dimensional and high-frequency time sequences in internet of things (IoT), Recently, convolutional neural networks (CNNs) have exhibited great superiority on TSC tasks in deep learning models, but not effective in capturing the long-term temporal dependency of time series. In this paper, we transform time series into GM-images by Gramian Angular Field (GAF) and Markov Transition Field (MTF), which can well preserve the temporal dependency and transition statistics of raw time series, respectively. We further propose an efficient Adaptive Dila-DenseNet (ADDN) to extract various and discernable patterns from GM-images for TSC. In ADDN, we devise an adaptive feature aggregation method for combining static and dynamic information flexibly from GAF and MTF representations. Moreover, inspired by the dilated residual networks, we design the Dila-Dense block in ADDN to preserve local spatial information for GM-images. The significant decrease of GM-images resolution in common deep learning models may lead to performance degradation. Nevertheless, our Dila-Dense block can address this resolution issue without decreasing the receptive field. Experiments evaluated on 24 benchmark datasets demonstrate that our approach shows greater efficiency and efficacy over the compared deep learning baselines, in IoT.