Machine learning-based approaches for large-scale temporal data analytics - Seyed Ali Majid Zonoozi (2019)
In many domains such as telecommunications, finance and sensor monitoring, large volumes of unlabeled temporal data are continuously generated in a sequential format, where the timestamps of the generated records are available. From a data analysis standpoint, there is significant utility to be gained by discovering the patterns in the data, in order to understand the trend of changes in the past and future. In this study, by considering different types of changes that occur in temporal data, including changes in underlying data distribution, recurring concepts and outliers, different machine learning-based models are proposed to track the changes, predict future values and detect abnormal patterns in temporal settings.