Light Weight Dilated CNN for Time Series Classification and Prediction

Time series data is available from a diverse set of sensors in real life. It is of prime importance in the domain of machine learning and artificial intelligence to analyze such data and identify outliers or anomalies, characteristic of the underlying activities and predict the future. Traditionally, time-series analysis involves identifying features using exploratory data analysis and using statistical approaches for classification and prediction. However, with the advent of convolutional neural networks (CNN), our ability to extract features automatically has substantially improved. In this paper, we propose a novel lightweight deep learning architecture of dilated CNN for classification and predicting time series data sets. We evaluate our model on a real-world human activity recognition time series data set and a synthetically crafted pseudo-realistic dataset for human intent recognition. Our model outperforms the state-of-the-art models and is light-weight.