An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series Classification

Understand

Time series data play an important role in many applications and their analysis reveals crucial information for understanding the underlying processes.

• Among the many time series learning tasks of great importance, we here focus on semi-supervised learning based on a graph representation of the data.
• Two main aspects are involved in this task.
• A suitable distance measure to evaluate the similarities between time series, and a learning method to make predictions based on these distances.