发表机构
LG AI Research（LG人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出专为金融预测定制的时间序列基础模型EXAONE Finance，采用无注意力线性时间算子架构，在基准FinVerse上取得最优性能。
AI 中文摘要
本技术报告介绍了EXAONE 金融预测模型（EXAONE Finance），这是一款专为金融预测定制的金融时间序列基础模型（TSFM）。近期的时间序列基础模型通过大规模预训练实现了出色的零样本性能，但它们主要针对通用领域的时间序列开发，且大多依赖自注意力骨干网络，其计算成本随序列长度和变量数量呈二次增长。此外，这些模型假设输入完全可观测，且预训练所用的语料库未能捕捉金融市场的独特动态，这些限制阻碍了它们在金融领域的应用——金融领域常见的是长序列、多变量、存在间歇性缺失的面板数据。为应对这些挑战，EXAONE Finance采用了无注意力架构，用两个简单且高效的线性时间算子替代自注意力：1）用于时间混合的因果一维卷积；2）用于变量混合的组感知池化多层感知机（MLP）。此外，掩码上下文增强技术让模型在训练过程中接触连续缺失区间，提升其对金融市场普遍存在的数据缺失情况的鲁棒性。EXAONE Finance在涵盖股票、外汇、大宗商品、加密资产、固定收益及宏观经济指标的大规模金融语料库上进行预训练，在涵盖各类资产类别的金融预测基准FinVerse上，该模型达到了当前最优性能，在点预测准确率、横截面资产排名和投资组合盈利能力三个评估层级中均排名第一。
英文摘要
This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series foundation model (TSFM) tailored to financial forecasting. While recent TSFMs achieve strong zero-shot performance through large-scale pretraining, they are primarily developed for general-domain time series and largely rely on self-attention backbones whose computational cost grows quadratically with sequence length and variate count. Moreover, they assume fully observed inputs and are pretrained on corpora that fail to adequately capture the unique dynamics of financial markets. These limitations hinder their applicability to finance, where long, many-channel, intermittently observed panels are common. To address these challenges, EXAONE Finance adopts an attention-free architecture, replacing self-attention with two simple yet effective linear-time operators: (1) a causal 1D convolution for temporal mixing and (2) a group-aware pooling multi-layer perceptron (MLP) for variate mixing. Furthermore, a masked-context augmentation exposes the model to contiguous missing spans during training, improving robustness to the missingness pervasive in financial markets. EXAONE Finance is pretrained on a synthetic financial corpus whose generative process is designed to reproduce the properties of financial series such as heavy tails, volatility clustering, jumps, regime shifts, and cross-asset dependence, combined with a domain-agnostic synthetic source. On FinVerse, a financial forecasting benchmark covering diverse asset classes, EXAONE Finance attains state-of-the-art performance, ranking first across all three evaluation tiers: point-forecast accuracy, cross-sectional asset ranking, and portfolio profitability.
CommentsTechnical report of EXAONE Finance 1.0
