Convolutional Neural Networks on Time Series for Smartphone Application Activations Using Wavelet Transform

This research focuses on time series of smartphone application activation (SAA) analyses. The inputs are big data of three categories of time series automatically collected from a single source panel of individuals from smartphones. The types of consumer activation outputs are precategorized. This study extracts effective features to identify categories of individual website access activities. This is an important but challenging task in the age of digital marketing. Most existing studies rely on conventional simple statistical analyses, time-series analyses, or shallow neural networks that cannot identify features to accurately classify plural categories of website access activities. This study proposes an automatic feature learning model from the raw inputs for the SAA problem by using a deep convolutional neural network (CNN) and Bayes optimization. Data are non-converted or converted with two levels by wavelet transformation for possible application in digital marketing.