Interactive time series visualization is essential in domains like IoT monitoring but is often constrained by latency and scalability challenges. Various methods have been proposed to address these issues, each with different trade-offs between efficiency, interactivity, and visualization accuracy, making systematic evaluation crucial. Traditional benchmarking approaches, however, fail to capture user-perceived responsiveness and accuracy in real-world exploration scenarios. To bridge this gap, we introduce TimeVizBench, an interactive evaluation platform for scalable time series visualization methods across performance and accuracy dimensions. TimeVizBench enables users to configure different methods, explore visual outputs interactively, and dynamically assess performance and accuracy. It also provides a standardized interface for integrating and comparing additional methods.