In recent years, Time Series (TS) analysis has attracted widespread attention in the community of Data Mining due to its special data format and broad application scenarios. An important aspect in TS analysis is Time Series Classification (TSC), which has been applied in medical diagnosis, human activity recognition, industrial troubleshooting, etc. Typically, all TSC work trains a stable model from an off-line TS dataset, without considering potential Concept Drift in streaming context. Conventional data stream is considered as independent examples (e.g., row data) coming in real-time, but rarely considers Time Series with real-valued data coming in a sequential order, called Time Series Stream. Processing such type of data, requires combining techniques in both communities of Time Series (TS) and Data Streams. To facilitate the users’ understanding of this combination, we propose ISETS, a web-based application which allows users to monitor the evolution of interpretable features in Time Series Stream.