mlsanityData to 14 Oct 2026

How Effective are Large Time Series Models in Hydrology? A Study on Water Level Forecasting in Everglades

arXiv 2505.01415 ↗First posted 2 May 2025cs.LGcited by 3

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Abstract (arXiv)

The Everglades play a crucial role in flood and drought regulation, water resource planning, and ecosystem management in the surrounding regions. However, traditional physics-based and statistical methods for predicting water levels often face significant challenges, including high computational costs and limited adaptability to diverse or unforeseen conditions. Recent advancements in large time series models have demonstrated the potential to address these limitations, with state-of-the-art deep learning and foundation models achieving remarkable success in time series forecasting across various domains. Despite this progress, their application to critical environmental systems, such as the Everglades, remains underexplored. In this study, we fill the gap by investigating twelve task-specific models and five time series foundation models across six categories for a real-world application focused on water level prediction in the Everglades. Our primary results show that the foundation model Chronos significantly outperforms all other models while the remaining foundation models exhibit relatively poor performance. We also noticed that the performance of task-specific models varies with the model architectures, and discussed the possible reasons. We hope our study and findings will inspire the community to explore the applicability of large time series models in hydrological applications. The code and data are available at https://github.com/rahuul2992000/Everglades-Benchmark.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 23 of its 51 references that are in the map. First posted on arXiv 2 May 2025.

Builds on

23 of 51 references.

References · 23

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  5. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 931
  6. 2024Chronos: Learning the Language of Time Seriescited by 759
  7. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 753
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  12. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 607
11 more
  1. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 482
  2. 2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 346
  3. 2023TSMixer: An All-MLP Architecture for Time Series Forecastingcited by 240
  4. 2024Timer: Generative Pre-trained Transformers Are Large Time Series Modelscited by 208
  5. 2023TimeGPT-1cited by 158
  6. 2024Position: What Can Large Language Models Tell Us about Time Series Analysiscited by 47
  7. 2023Exploring Model Learning Heterogeneity for Boosting Ensemble Robustnesscited by 6
  8. 2024CoDiCast: Conditional Diffusion Model for Global Weather Prediction with Uncertainty Quantificationcited by 6
  9. 2024FIDLAR: Forecast-Informed Deep Learning Architecture for Flood Mitigationcited by 5
  10. 2024Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecastingcited by 5
  11. 2023Graph Transformer Network for Flood Forecasting with Heterogeneous Covariatescited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Retrieval-Augmented Foundation Models for Water Level Prediction in the Evergladescited by 3
  2. 2026Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problemscited by 0
  3. 2026Are Time-Series Foundation Models Ready for E-Nose Data? An Empirical Assessment of Their Embeddingscited by 0