Using DenseNet for IoT multivariate time series classification

Nowadays, most Internet of Things (IoT) devices collect multiple features and produce multivariate time series. In an IoT application, the mining and classification of the collected data have become crucial tasks. Hybrid LSTM-fully convolutional networks (MLSTM-FCN) provide state-of-the-art classification results on multivariate time series benchmarks. This paper examines the use of the DenseNet architecture, originally proposed for computer vision applications, for the classification of multivariate time series. More precisely, this paper proposes a hybrid LSTM-DenseNet model that is able to achieve the performance of the state-of-the-art models and surpass them in many situations, based on the results obtained from various experiments on 15 benchmark datasets. Thus, this paper suggests the 1D DenseNet as a potential tool to be considered by machine learning engineers and data scientists for IoT time series classification task.