mlsanityData to 14 Oct 2026

LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction

arXiv 2304.14343 ↗First posted 27 Apr 2023cs.LGcited by 20

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Abstract (arXiv)

As deep learning technology advances and more urban spatial-temporal data accumulates, an increasing number of deep learning models are being proposed to solve urban spatial-temporal prediction problems. However, there are limitations in the existing field, including open-source data being in various formats and difficult to use, few papers making their code and data openly available, and open-source models often using different frameworks and platforms, making comparisons challenging. A standardized framework is urgently needed to implement and evaluate these methods. To address these issues, we propose LibCity, an open-source library that offers researchers a credible experimental tool and a convenient development framework. In this library, we have reproduced 65 spatial-temporal prediction models and collected 55 spatial-temporal datasets, allowing researchers to conduct comprehensive experiments conveniently. By enabling fair model comparisons, designing a unified data storage format, and simplifying the process of developing new models, LibCity is poised to make significant contributions to the spatial-temporal prediction field.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). Builds on 48 of its 123 references that are in the map. First posted on arXiv 27 Apr 2023.

Builds on

48 of 123 references.

References · 48

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36 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 8; 2025: 7; 2026: 5.

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  1. 2024GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluationcited by 212
  2. 2024Timer: Generative Pre-trained Transformers Are Large Time Series Modelscited by 208
  3. 2025Chronos-2: From Univariate to Universal Forecastingcited by 194
  4. 2025fev-bench: A Realistic Benchmark for Time Series Forecastingcited by 64
  5. 2024In-Context Fine-Tuning for Time-Series Foundation Modelscited by 35
  6. 2024Towards Neural Scaling Laws for Time Series Foundation Modelscited by 33
  7. 2024AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Predictioncited by 25
  8. 2025Zero-Shot Time Series Forecasting with Covariates via In-Context Learningcited by 12
  9. 2026Revisiting the Generic Transformer: Deconstructing a Strong Baseline for Time Series Foundation Modelscited by 12
  10. 2026Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modelingcited by 10
  11. 2024Macroeconomic Forecasting with Large Language Modelscited by 8
  12. 2025TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysiscited by 5
8 more
  1. 2026KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoningcited by 5
  2. 2025Investigating Compositional Reasoning in Time Series Foundation Modelscited by 3
  3. 2024RePST: Language Model Empowered Spatio-Temporal Forecasting via Semantic-Oriented Reprogrammingcited by 2
  4. 2025Are Time-Indexed Foundation Models the Future of Time Series Imputation?cited by 1
  5. 2024UMOD: A Novel and Effective Urban Metro Origin-Destination Flow Prediction Methodcited by 0
  6. 2025STX-Search: Explanation Search for Continuous Dynamic Spatio-Temporal Modelscited by 0
  7. 2026REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecastingcited by 0
  8. 2026On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Modelscited by 0