Comparative Analysis of LSTM-FCN on Multiple Datasets
Shad Akhtar, Munam Ali Shah · IET conference proceedings. · 2021
Classification of time series data is a critical problem. With the growth of time series data, several algorithms have been proposed. The deep learning technique Long Short-Term Memory (LSTM) with Fully Convolutional Networks (FCN) is widely used for the classification of time series data. The use of LSTM-FCN to improve fully convolutional networks. Through attention mechanism visualisation of context, the vector is performed and enhances the results of time series classification. The aim of this research is to compare the results of LSTM-FCN output on a multiple dataset. The results show that the selected technique is more effective at classifying time series. Visualisation is given for the performance analysis of the LSTM-FCN technique on all datasets.