Jungmin Kim

2024

Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement Learning

We are finding a temporal pooling automatically reflecting the diverse characteristics of time series data in time series classification task. Previous temporal poolings exhibit data dependency based on segmentation type, and each excels with different time series data characteristics. Selection over Multiple Temporal Poolings (SoM-TP) employs selection ensemble learning to utilize the perspective of each temporal pooling in a data driven way. The diverse perspective learning of SoM-TP addresses the drawbacks of a single pooling perspective while also incorporating the benefits of the best-performing temporal pooling.