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Recent advancements in large language models have spurred significant developments in Time Series Foundation Models (TSFMs). These models claim great promise in performing zero-shot forecasting without the need for specific training, leveraging the extensive "corpus" of time-series data they have been trained on. Forecasting is crucial in predictive building analytics, presenting substantial untapped potential for TSFMS in this domain. However, time-series data are often domain-specific and governed by diverse factors such as deployment environments, sensor characteristics, sampling rate, and data resolution, which complicates generalizability of these models across different contexts. Thus, while language models benefit from the relative uniformity of text data, TSFMs face challenges in learning from heterogeneous and contextually varied time-series data to ensure accurate and reliable performance in various applications. This paper seeks to understand how recently developed TSFMs perform in the building domain, particularly concerning their generalizability. We benchmark these models on three large datasets related to indoor air temperature and electricity usage. Our results indicate that TSFMs exhibit marginally better performance compared to statistical models on unseen sensing modality and/or patterns. Based on the benchmark results, we also provide insights for improving future TSFMs on building analytics. 

Cyber-physical systems are starting to adopt neural network (NN) models for a variety of smart sensing applications. While several efforts seek better NN architectures for system performance improvement, few attempts have been made to study the deployment of these systems in the field. Proper deployment of these systems is critical to achieving ideal performance, but the current practice is largely empirical via trials and errors, lacking a measure of quality. Sensing quality should reflect the impact on the performance of NN models that drive machine perception tasks. However, traditional approaches either evaluate statistical difference that exists objectively, or model the quality subjectively via human perception. In this work, we propose an efficient sensing quality measure requiring limited data samples using smart voice sensing system as an example. We adopt recent techniques in uncertainty evaluation for NN to estimate audio sensing quality. Intuitively, a deployment at better sensing location should lead to less uncertainty in NN predictions. We design SQEE, Sensing Quality Evaluation at the Edge for NN models, which constructs a model ensemble through Monte-Carlo dropout and estimates posterior total uncertainty via average conditional entropy. We collected data from three indoor environments, with a total of 148 transmitting-receiving (t-r) locations experimented and more than 7,000 examples tested. SQEE achieves the best performance in terms of the top-1 ranking accuracy---whether the measure finds the best spot for deployment, in comparison with other uncertainty strategies. We implemented SQEE on a ReSpeaker to study SQEE's real-world efficacy. Experimental result shows that SQEE can effectively evaluate the data collected from each t-r location pair within 30 seconds and achieve an average top-3 ranking accuracy of over 94%. We further discuss generalization of our framework to other sensing schemes. 

Real-world applications often involve irregular time series, for which the time intervals between successive observations are non-uniform. Irregularity across multiple features in a multi-variate time series further results in a different subset of features at any given time (i.e., asynchronicity). Existing pre-training schemes for time-series, however, often assume regularity of time series and make no special treatment of irregularity. We argue that such irregularity offers insight about domain property of the data—for example, frequency of hospital visits may signal patient health condition—that can guide representation learning. In this work, we propose PrimeNet to learn a self-supervised representation for irregular multivariate time-series. Specifically, we design a timesensitive contrastive learning and data reconstruction task to pre-train a model. Irregular time-series exhibits considerable variations in sampling density over time. Hence, our triplet generation strategy follows the density of the original data points, preserving its native irregularity. Moreover, the sampling density variation over time makes data reconstruction difficult for different regions. Therefore, we design a data masking technique that always masks a constant time duration to accommodate reconstruction for regions of different sampling density. We learn with these tasks using unlabeled data to build a pre-trained model and fine-tune on a downstream task with limited labeled data, in contrast with existing fully supervised approach for irregular time-series, requiring large amounts of labeled data. Experiment results show that PrimeNet significantly outperforms state-of-the-art methods on naturally irregular and asynchronous data from Healthcare and IoT applications for several downstream tasks, including classification, interpolation, and regression. 

Emerging building analytics rely on data-driven machine learning algorithms. However, writing these analytics is still challenging— developers need to know not only what data are required by the analytics but also how to reach the data in each individual building, despite the existing solutions to standardizing data and resource management in buildings. To bridge the gap between analytics development and the specific details of reaching actual data in each building, we present Energon, an open-source system that enables portable building analytics. The core of Energon is a new data organization for building as well as tools that can effectively manage building data and support building analytics development. More specifically, we propose a new "logic partition" of data resources in buildings, and this abstraction universally applies to all buildings. We develop a declarative query language accordingly to f ind data resources in this new logic view with high-level queries, thus substantially reducing development efforts. We also develop a query engine with automatic data extraction by traversing building ontology that widely exists in buildings. In this way, Energon enables mapping of analytics requirements to building resources in a building-agnostic manner. Using four types of real-world building analytics, we demonstrate the use of Energon and its effectiveness in reducing development efforts.