A Survey of Deep Learning and Foundation Models for Time Series Forecasting
作者: John A. Miller, Mohammed Aldosari, Farah Saeed, Nasid Habib Barna, Subas Rana, I. Budak Arpinar, Ninghao Liu
分类: cs.LG
发布日期: 2024-01-25
💡 一句话要点
综述深度学习与基础模型在时间序列预测中的应用与挑战
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 时间序列预测 深度学习 基础模型 知识图谱 疫情预测 模型可解释性 机器学习
📋 核心要点
- 现有时间序列预测方法在疫情预测等领域面临数据不足和模型可解释性等挑战。
- 论文提出利用基础模型和知识图谱等方法，增强深度学习模型的知识获取能力和适应性。
- 通过对多种先进技术的综述，论文指出了深度学习在时间序列预测中的潜在优势和未来研究方向。
📝 摘要（中文）
深度学习在多个应用领域取得了成功，但在时间序列预测中的优势尚未显现。尽管传统统计或机器学习技术的混合方法在Makridakis竞赛中逐渐成为顶尖表现者，但深度学习在疫情预测等领域仍面临挑战，如训练数据不足、缺乏科学知识的积累和模型可解释性。基础模型的开发使得模型能够在数据不足时理解模式并获取知识。本文综述了多种先进建模技术，并提出了进一步研究的建议。
🔬 方法详解
问题定义：论文要解决的具体问题是深度学习在时间序列预测中的应用受限于训练数据不足、缺乏科学知识积累和模型可解释性等痛点。
核心思路：论文的核心解决思路是通过开发基础模型，利用大规模预训练的深度学习模型来理解模式和获取知识，从而在数据不足的情况下提升模型性能。
技术框架：整体架构包括基础模型的构建、知识图谱的整合和深度学习模型的训练。主要模块包括数据预处理、模型设计、知识注入和模型评估。
关键创新：最重要的技术创新点在于引入基础模型和知识图谱，使得模型能够在缺乏大量训练数据时仍能有效学习和预测，这与传统方法的依赖大量标注数据的本质区别。
关键设计：关键设计包括选择合适的损失函数以适应时间序列数据的特性，采用适当的网络结构（如变换器和图神经网络）来增强模型的表达能力。
🖼️ 关键图片
📊 实验亮点
论文通过综述多种先进的深度学习技术，指出基础模型在时间序列预测中的应用潜力。尽管具体实验结果未详述，但强调了深度学习模型在处理复杂时间序列数据时的优势，尤其是在数据稀缺的情况下，能够显著提升预测性能。
🎯 应用场景
该研究的潜在应用领域包括金融市场预测、气候变化分析和公共卫生监测等。通过提升时间序列预测的准确性，能够为决策者提供更可靠的数据支持，进而影响政策制定和资源分配。未来，随着基础模型和知识图谱的进一步发展，深度学习在时间序列预测中的应用将更加广泛。
📄 摘要（原文）
Deep Learning has been successfully applied to many application domains, yet its advantages have been slow to emerge for time series forecasting. For example, in the well-known Makridakis (M) Competitions, hybrids of traditional statistical or machine learning techniques have only recently become the top performers. With the recent architectural advances in deep learning being applied to time series forecasting (e.g., encoder-decoders with attention, transformers, and graph neural networks), deep learning has begun to show significant advantages. Still, in the area of pandemic prediction, there remain challenges for deep learning models: the time series is not long enough for effective training, unawareness of accumulated scientific knowledge, and interpretability of the model. To this end, the development of foundation models (large deep learning models with extensive pre-training) allows models to understand patterns and acquire knowledge that can be applied to new related problems before extensive training data becomes available. Furthermore, there is a vast amount of knowledge available that deep learning models can tap into, including Knowledge Graphs and Large Language Models fine-tuned with scientific domain knowledge. There is ongoing research examining how to utilize or inject such knowledge into deep learning models. In this survey, several state-of-the-art modeling techniques are reviewed, and suggestions for further work are provided.