Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data

• URL: http://arxiv.org/abs/2412.00403v1
• Date: Sat, 30 Nov 2024 09:00:24 GMT
• Title: Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data
• Authors: Yuwei Fan, Tao Song, Chenlong Feng, Keyu Song, Chao Liu, Dongxiang Jiang,
• Abstract summary: This paper explores the application of a pre-trained large time series model, Timer, in the prediction of SCADA data collected from wind turbines. The model was fine-tuned on datasets sourced from two wind farms, which exhibited differing characteristics.
• Score: 6.296946118570559
• License:
• Abstract: The remarkable achievements of large models in the fields of natural language processing (NLP) and computer vision (CV) have sparked interest in their application to time series forecasting within industrial contexts. This paper explores the application of a pre-trained large time series model, Timer, which was initially trained on a wide range of time series data from multiple domains, in the prediction of Supervisory Control and Data Acquisition (SCADA) data collected from wind turbines. The model was fine-tuned on SCADA datasets sourced from two wind farms, which exhibited differing characteristics, and its accuracy was subsequently evaluated. Additionally, the impact of data volume was studied to evaluate the few-shot ability of the Timer. Finally, an application study on one-turbine fine-tuning for whole-plant prediction was implemented where both few-shot and cross-turbine generalization capacity is required. The results reveal that the pre-trained large model does not consistently outperform other baseline models in terms of prediction accuracy whenever the data is abundant or not, but demonstrates superior performance in the application study. This result underscores the distinctive advantages of the pre-trained large time series model in facilitating swift deployment.

Related papers

• 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)
• DAM: Towards A Foundation Model for Time Series Forecasting [0.8231118867997028]
  We propose a neural model that takes randomly sampled histories and outputs an adjustable basis composition as a continuous function of time. It involves three key components: (1) a flexible approach for using randomly sampled histories from a long-tail distribution; (2) a transformer backbone that is trained on these actively sampled histories to produce, as representational output; and (3) the basis coefficients of a continuous function of time.
  arXiv  Detail & Related papers  (2024-07-25T08:48:07Z)
• TimeGPT in Load Forecasting: A Large Time Series Model Perspective [38.92798207166188]
  Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. This paper aims to discuss the potential of large time series models in load forecasting with scarce historical data.
  arXiv  Detail & Related papers  (2024-04-07T09:05:09Z)
• Probing the Robustness of Time-series Forecasting Models with CounterfacTS [1.823020744088554]
  We present and publicly release CounterfacTS, a tool to probe the robustness of deep learning models in time-series forecasting tasks. CounterfacTS has a user-friendly interface that allows the user to visualize, compare and quantify time series data and their forecasts.
  arXiv  Detail & Related papers  (2024-03-06T07:34:47Z)
• Unified Training of Universal Time Series Forecasting Transformers [104.56318980466742]
  We present a Masked-based Universal Time Series Forecasting Transformer (Moirai) Moirai is trained on our newly introduced Large-scale Open Time Series Archive (LOTSA) featuring over 27B observations across nine domains. Moirai achieves competitive or superior performance as a zero-shot forecaster when compared to full-shot models.
  arXiv  Detail & Related papers  (2024-02-04T20:00:45Z)
• Timer: Generative Pre-trained Transformers Are Large Time Series Models [83.03091523806668]
  This paper aims at the early development of large time series models (LTSM) During pre-training, we curate large-scale datasets with up to 1 billion time points. To meet diverse application needs, we convert forecasting, imputation, and anomaly detection of time series into a unified generative task.
  arXiv  Detail & Related papers  (2024-02-04T06:55:55Z)
• 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)
• 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)

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.