Deep Learning Foundation and Pattern Models: Challenges in Hydrological Time Series

• URL: http://arxiv.org/abs/2410.15218v2
• Date: Sun, 09 Mar 2025 21:54:42 GMT
• Title: Deep Learning Foundation and Pattern Models: Challenges in Hydrological Time Series
• Authors: Junyang He, Ying-Jung Chen, Alireza Jafari, Anushka Idamekorala, Geoffrey Fox,
• Abstract summary: This paper aims to identify key features in time series by examining hydrology data.<n>This research analyzes hydrology time series from the CAMELS and Caravan global datasets.
• Score: 1.4854797901022863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There has been active investigation into deep learning approaches for time series analysis, including foundation models. However, most studies do not address significant scientific applications. This paper aims to identify key features in time series by examining hydrology data. Our work advances computer science by emphasizing critical application features and contributes to hydrology and other scientific fields by identifying modeling approaches that effectively capture these features. Scientific time series data are inherently complex, involving observations from multiple locations, each with various time-dependent data streams and exogenous factors that may be static or time-varying and either application-dependent or purely mathematical. This research analyzes hydrology time series from the CAMELS and Caravan global datasets, which encompass rainfall and runoff data across catchments, featuring up to six observed streams and 209 static parameters across approximately 8,000 locations. Our investigation assesses the impact of exogenous data through eight different model configurations for key hydrology tasks. Results demonstrate that integrating exogenous information enhances data representation, reducing mean squared error by up to 40% in the largest dataset. Additionally, we present a detailed performance comparison of over 20 state-of-the-art pattern and foundation models. The analysis is fully open-source, facilitated by Jupyter Notebook on Google Colab for LSTM-based modeling, data preprocessing, and model comparisons. Preliminary findings using alternative deep learning architectures reveal that models incorporating comprehensive observed and exogenous data outperform more limited approaches, including foundation models. Notably, natural annual periodic exogenous time series contribute the most significant improvements, though static and other periodic factors are also valuable.

Related papers

• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [55.09326865401653]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models [104.17057231661371]
  Time series analysis is crucial for understanding dynamics of complex systems. Recent advances in foundation models have led to task-agnostic Time Series Foundation Models (TSFMs) and Large Language Model-based Time Series Models (TSLLMs) Their success depends on large, diverse, and high-quality datasets, which are challenging to build due to regulatory, diversity, quality, and quantity constraints. This survey provides a comprehensive review of synthetic data for TSFMs and TSLLMs, analyzing data generation strategies, their role in model pretraining, fine-tuning, and evaluation, and identifying future research directions.
  arXiv  Detail & Related papers  (2025-03-14T13:53:46Z)
• TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting [59.702504386429126]
  TimeRAF is a Retrieval-Augmented Forecasting model that enhance zero-shot time series forecasting through retrieval-augmented techniques. TimeRAF employs an end-to-end learnable retriever to extract valuable information from the knowledge base.
  arXiv  Detail & Related papers  (2024-12-30T09:06:47Z)
• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• Deep Time Series Models: A Comprehensive Survey and Benchmark [74.28364194333447]
  Time series data is of great significance in real-world scenarios. Recent years have witnessed remarkable breakthroughs in the time series community. We release Time Series Library (TSLib) as a fair benchmark of deep time series models for diverse analysis tasks.
  arXiv  Detail & Related papers  (2024-07-18T08:31:55Z)
• A Survey on Diffusion Models for Time Series and Spatio-Temporal Data [92.1255811066468]
  We review the use of diffusion models in time series and S-temporal data, categorizing them by model, task type, data modality, and practical application domain. We categorize diffusion models into unconditioned and conditioned types discuss time series and S-temporal data separately. Our survey covers their application extensively in various fields including healthcare, recommendation, climate, energy, audio, and transportation.
  arXiv  Detail & Related papers  (2024-04-29T17:19:40Z)
• Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook [95.32949323258251]
  Temporal data, notably time series andtemporal-temporal data, are prevalent in real-world applications. Recent advances in large language and other foundational models have spurred increased use in time series andtemporal data mining.
  arXiv  Detail & Related papers  (2023-10-16T09:06:00Z)
• Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain [54.67888148566323]
  We introduce three large-scale time series forecasting datasets from the cloud operations domain. We show it is a strong zero-shot baseline and benefits from further scaling, both in model and dataset size. Accompanying these datasets and results is a suite of comprehensive benchmark results comparing classical and deep learning baselines to our pre-trained method.
  arXiv  Detail & Related papers  (2023-10-08T08:09:51Z)
• MADS: Modulated Auto-Decoding SIREN for time series imputation [9.673093148930874]
  We propose MADS, a novel auto-decoding framework for time series imputation, built upon implicit neural representations. We evaluate our model on two real-world datasets, and show that it outperforms state-of-the-art methods for time series imputation.
  arXiv  Detail & Related papers  (2023-07-03T09:08:47Z)
• Data Augmentation techniques in time series domain: A survey and taxonomy [0.20971479389679332]
  Deep neural networks used to work with time series heavily depend on the size and consistency of the datasets used in training. This work systematically reviews the current state-of-the-art in the area to provide an overview of all available algorithms. The ultimate aim of this study is to provide a summary of the evolution and performance of areas that produce better results to guide future researchers in this field.
  arXiv  Detail & Related papers  (2022-06-25T17:09:00Z)
• Structured Time Series Prediction without Structural Prior [0.152292571922932]
  Time series prediction is a widespread and well studied problem with applications in many domains. We propose a fully data-driven model which does not rely on such a graph, nor any other prior structural information.
  arXiv  Detail & Related papers  (2022-02-07T22:01:58Z)
• Time Series Analysis via Network Science: Concepts and Algorithms [62.997667081978825]
  This review provides a comprehensive overview of existing mapping methods for transforming time series into networks. We describe the main conceptual approaches, provide authoritative references and give insight into their advantages and limitations in a unified notation and language. Although still very recent, this research area has much potential and with this survey we intend to pave the way for future research on the topic.
  arXiv  Detail & Related papers  (2021-10-11T13:33:18Z)
• An Experimental Review on Deep Learning Architectures for Time Series Forecasting [0.0]
  We provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more efficient.
  arXiv  Detail & Related papers  (2021-03-22T17:58:36Z)
• Global Models for Time Series Forecasting: A Simulation Study [2.580765958706854]
  We simulate time series from simple data generating processes (DGP), such as Auto Regressive (AR) and Seasonal AR, to complex DGPs, such as Chaotic Logistic Map, Self-Exciting Threshold Auto-Regressive, and Mackey-Glass equations. The lengths and the number of series in the dataset are varied in different scenarios. We perform experiments on these datasets using global forecasting models including Recurrent Neural Networks (RNN), Feed-Forward Neural Networks, Pooled Regression (PR) models, and Light Gradient Boosting Models (LGBM)
  arXiv  Detail & Related papers  (2020-12-23T04:45:52Z)
• Learning summary features of time series for likelihood free inference [93.08098361687722]
  We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
  arXiv  Detail & Related papers  (2020-12-04T19:21:37Z)
• Deep learning for time series classification [2.0305676256390934]
  Time series analysis allows us to visualize and understand the evolution of a process over time. Time series classification consists of constructing algorithms dedicated to automatically label time series data. Deep learning has emerged as one of the most effective methods for tackling the supervised classification task.
  arXiv  Detail & Related papers  (2020-10-01T17:38:40Z)
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks [91.65637773358347]
  We propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module. Our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets.
  arXiv  Detail & Related papers  (2020-05-24T04:02:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.