FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting

• URL: http://arxiv.org/abs/2410.11802v3
• Date: Fri, 01 Nov 2024 05:14:40 GMT
• Title: FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting
• Authors: Zhe Li, Xiangfei Qiu, Peng Chen, Yihang Wang, Hanyin Cheng, Yang Shu, Jilin Hu, Chenjuan Guo, Aoying Zhou, Qingsong Wen, Christian S. Jensen, Bin Yang,
• Abstract summary: Time Series Forecasting (TSF) is key functionality in numerous fields, including in finance, weather services, and energy management. Foundation models exhibit promising inferencing capabilities in new or unseen data. We propose a new benchmark, FoundTS, to enable thorough and fair evaluation and comparison of such models.
• Score: 44.33565276128137
• License:
• Abstract: Time Series Forecasting (TSF) is key functionality in numerous fields, including in finance, weather services, and energy management. While TSF methods are emerging these days, many of them require domain-specific data collection and model training and struggle with poor generalization performance on new domains. Foundation models aim to overcome this limitation. Pre-trained on large-scale language or time series data, they exhibit promising inferencing capabilities in new or unseen data. This has spurred a surge in new TSF foundation models. We propose a new benchmark, FoundTS, to enable thorough and fair evaluation and comparison of such models. FoundTS covers a variety of TSF foundation models, including those based on large language models and those pretrained on time series. Next, FoundTS supports different forecasting strategies, including zero-shot, few-shot, and full-shot, thereby facilitating more thorough evaluations. Finally, FoundTS offers a pipeline that standardizes evaluation processes such as dataset splitting, loading, normalization, and few-shot sampling, thereby facilitating fair evaluations. Building on this, we report on an extensive evaluation of TSF foundation models on a broad range of datasets from diverse domains and with different statistical characteristics. Specifically, we identify pros and cons and inherent limitations of existing foundation models, and we identify directions for future model design. We make our code and datasets available at https://anonymous.4open.science/r/FoundTS-C2B0.

Related papers

• In-Context Fine-Tuning for Time-Series Foundation Models [18.348874079298298]
  In particular, we design a pretrained foundation model that can be prompted with multiple time-series examples. Our foundation model is specifically trained to utilize examples from multiple related time-series in its context window. We show that such a foundation model that uses in-context examples at inference time can obtain much better performance on popular forecasting benchmarks.
  arXiv  Detail & Related papers  (2024-10-31T16:20:04Z)
• GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation [90.53485251837235]
  Time series foundation models excel in zero-shot forecasting, handling diverse tasks without explicit training. GIFT-Eval is a pioneering benchmark aimed at promoting evaluation across diverse datasets. GIFT-Eval encompasses 23 datasets over 144,000 time series and 177 million data points.
  arXiv  Detail & Related papers  (2024-10-14T11:29:38Z)
• High-Performance Few-Shot Segmentation with Foundation Models: An Empirical Study [64.06777376676513]
  We develop a few-shot segmentation (FSS) framework based on foundation models. To be specific, we propose a simple approach to extract implicit knowledge from foundation models to construct coarse correspondence. Experiments on two widely used datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-09-10T08:04:11Z)
• 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)
• Understanding Different Design Choices in Training Large Time Series Models [71.20102277299445]
  Training Large Time Series Models (LTSMs) on heterogeneous time series data poses unique challenges. We propose emphtime series prompt, a novel statistical prompting strategy tailored to time series data. We introduce textttLTSM-bundle, which bundles the best design choices we have identified.
  arXiv  Detail & Related papers  (2024-06-20T07:09:19Z)
• NuwaTS: a Foundation Model Mending Every Incomplete Time Series [24.768755438620666]
  We present textbfNuwaTS, a novel framework that repurposes Pre-trained Language Models for general time series imputation. NuwaTS can be applied to impute missing data across any domain. We show that NuwaTS generalizes to other time series tasks, such as forecasting.
  arXiv  Detail & Related papers  (2024-05-24T07:59:02Z)
• Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting [54.04430089029033]
  We present Lag-Llama, a general-purpose foundation model for time series forecasting based on a decoder-only transformer architecture. Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities. When fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-10-12T12:29:32Z)
• Toward a Foundation Model for Time Series Data [34.1973242428317]
  A foundation model is a machine learning model trained on a large and diverse set of data. We develop an effective time series foundation model by leveraging unlabeled samples from multiple domains.
  arXiv  Detail & Related papers  (2023-10-05T21:44:50Z)
• Unified Long-Term Time-Series Forecasting Benchmark [0.6526824510982802]
  We present a comprehensive dataset designed explicitly for long-term time-series forecasting. We incorporate a collection of datasets obtained from diverse, dynamic systems and real-life records. To determine the most effective model in diverse scenarios, we conduct an extensive benchmarking analysis using classical and state-of-the-art models. Our findings reveal intriguing performance comparisons among these models, highlighting the dataset-dependent nature of model effectiveness.
  arXiv  Detail & Related papers  (2023-09-27T18:59:00Z)

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.