# BTS: Building Timeseries Dataset: Empowering Large-Scale Building Analytics

arXiv: https://arxiv.org/abs/2406.08990 · HTML: https://mlsanity.com/paper/2406.08990/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 96 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis](https://mlsanity.com/paper/2210.02186/) (2022)
- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://mlsanity.com/paper/2310.06625/) (2023)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [Large Language Models Are Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2310.07820/) (2023)
- [MM-LLMs: Recent Advances in MultiModal Large Language Models](https://mlsanity.com/paper/2401.13601/) (2024)
- [ForecastPFN: Synthetically-Trained Zero-Shot Forecasting](https://mlsanity.com/paper/2311.01933/) (2023)
- [Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics](https://mlsanity.com/paper/2011.05987/) (2020)
- [Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data](https://mlsanity.com/paper/2206.02353/) (2022)
- [Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability](https://mlsanity.com/paper/2012.14349/) (2020)
- [Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting](https://mlsanity.com/paper/2302.09956/) (2023)
- [Utilizing Language Models for Energy Load Forecasting](https://mlsanity.com/paper/2310.17788/) (2023)
- [Transfer Learning for Thermal Comfort Prediction in Multiple Cities](https://mlsanity.com/paper/2004.14382/) (2020)
- [FADACS: A Few-shot Adversarial Domain Adaptation Architecture for Context-Aware Parking Availability Sensing](https://mlsanity.com/paper/2007.08551/) (2020)
- [Continually learning out-of-distribution spatiotemporal data for robust energy forecasting](https://mlsanity.com/paper/2306.06385/) (2023)
- [Understanding occupants' behaviour, engagement, emotion, and comfort indoors with heterogeneous sensors and wearables](https://mlsanity.com/paper/2105.06637/) (2021)
- [Data-Driven Key Performance Indicators and Datasets for Building Energy Flexibility: A Review and Perspectives](https://mlsanity.com/paper/2211.12252/) (2022)
- [A smart building semantic platform to enable data re-use in energy analytics applications: the Data Clearing House](https://mlsanity.com/paper/2311.11630/) (2023)

## Built on by (most cited first, 6 of 6)

- [Can Time-Series Foundation Models Perform Building Energy Management Tasks?](https://mlsanity.com/paper/2506.11250/) (2025)
- [Exploring Capabilities of Time Series Foundation Models in Building Analytics](https://mlsanity.com/paper/2411.08888/) (2024)
- [RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models](https://mlsanity.com/paper/2607.06504/) (2026)
- [TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting](https://mlsanity.com/paper/2607.06349/) (2026)
- [BTS-AgentBench: A Deterministic, Replayable Pipeline from Read-Only Telemetry Logs to Agent Benchmarks](https://mlsanity.com/paper/2608.27334/) (2026)
- [TimeNet: An Extensible Unified Data Infrastructure for Next-Generation Temporal Foundation Models](https://mlsanity.com/paper/2610.04407/) (2026)

## Abstract (arXiv)

Buildings play a crucial role in human well-being, influencing occupant comfort, health, and safety. Additionally, they contribute significantly to global energy consumption, accounting for one-third of total energy usage, and carbon emissions. Optimizing building performance presents a vital opportunity to combat climate change and promote human flourishing. However, research in building analytics has been hampered by the lack of accessible, available, and comprehensive real-world datasets on multiple building operations. In this paper, we introduce the Building TimeSeries (BTS) dataset. Our dataset covers three buildings over a three-year period, comprising more than ten thousand timeseries data points with hundreds of unique ontologies. Moreover, the metadata is standardized using the Brick schema. To demonstrate the utility of this dataset, we performed benchmarks on two tasks: timeseries ontology classification and zero-shot forecasting. These tasks represent an essential initial step in addressing challenges related to interoperability in building analytics. Access to the dataset and the code used for benchmarking are available here: https://github.com/cruiseresearchgroup/DIEF_BTS .