Time Weaver: A Conditional Time Series Generation Model

Imagine generating a city's electricity demand pattern based on weather, the presence of an electric vehicle, and location, which could be used for capacity planning during a winter freeze. Such real-world time series are often enriched with paired heterogeneous contextual metadata (weather, location, etc.). Current approaches to time series generation often ignore this paired metadata, and its heterogeneity poses several practical challenges in adapting existing conditional generation approaches from the image, audio, and video domains to the time series domain. To address this gap, we introduce Time Weaver, a novel diffusion-based model that leverages the heterogeneous metadata in the form of categorical, continuous, and even time-variant variables to significantly improve time series generation. Additionally, we show that naive extensions of standard evaluation metrics from the image to the time series domain are insufficient. These metrics do not penalize conditional generation approaches for their poor specificity in reproducing the metadata-specific features in the generated time series. Thus, we innovate a novel evaluation metric that accurately captures the specificity of conditional generation and the realism of the generated time series. We show that Time Weaver outperforms state-of-the-art benchmarks, such as Generative Adversarial Networks (GANs), by up to 27% in downstream classification tasks on real-world energy, medical, air quality, and traffic data sets.

Safe Networked Robotics via Formal Verification

Autonomous robots must utilize rich sensory data to make safe control decisions. Often, compute-constrained robots require assistance from remote computation (''the cloud'') if they need to invoke compute-intensive Deep Neural Network perception or control models. Likewise, a robot can be remotely teleoperated by a human during risky scenarios. However, this assistance comes at the cost of a time delay due to network latency, resulting in stale/delayed observations being used in the cloud to compute the control commands for the present robot state. Such communication delays could potentially lead to the violation of essential safety properties, such as collision avoidance. This paper develops methods to ensure the safety of teleoperated robots with stochastic latency. To do so, we use tools from formal verification to construct a shield (i.e., run-time monitor) that provides a list of safe actions for any delayed sensory observation, given the expected and worst-case network latency. Our shield is minimally intrusive and enables networked robots to satisfy key safety constraints, expressed as temporal logic specifications, with high probability. Our approach gracefully improves a teleoperated robot's safety vs. efficiency trade-off as a function of network latency, allowing us to quantify performance gains for WiFi or even future 5G networks. We demonstrate our approach on a real F1/10th autonomous vehicle that navigates in crowded indoor environments and transmits rich LiDAR sensory data over congested WiFi links.