Advanced classification for streaming time series and data streams - Nguyen, Hai Long (2013)
Nowadays, overwhelming volumes of sequential data are very common in scientific and business applications, such as biomedicine, stock markets, retail industry, and communication networks. Time series and data streams are the two most popular types of sequential data. The main difference between them is that time series is on a single variable domain, while data streams are generally on a multivariate domain. However, they do share some unique characteristics: possibly infinite volume, time-ordered and dynamically changing. In this dissertation, we propose classification algorithms for time series and data streams that satisfy strict constraints, such as bounded memory, single pass, real-time response, and concept-drift detection. Here, a concept drift refers to the situation where the data's underlying distribution changes over time.