用大模型分析文本信息，提升节假日销量预测准确率
LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data
- 用大语言模型融合文本与历史数据，改进现有预测流程
- 在零售业大规模应用中显著提升节日期间销量预测精度
- 适合需要结合新闻、产品描述等非结构化数据的预测场景
现代时间序列预测模型往往未能充分利用时间序列本身的丰富非结构化信息。这种缺乏有效条件约束会导致明显模型失效，例如模型可能不了解某产品的具体特性，从而无法提前预判节假日等外部事件带来的销量激增。为此，本文提出一种新型预测后处理方法——LLMForecaster，通过微调大语言模型，将非结构化语义和上下文信息与历史数据结合，以改进现有需求预测流水线的输出。在工业级零售应用中，该方法在多个受节假日驱动销量波动的产品集上均实现了统计显著的预测性能提升。
原文摘要 · Abstract (English)
Modern time-series forecasting models often fail to make full use of rich unstructured information about the time series themselves. This lack of proper conditioning can lead to obvious model failures; for example, models may be unaware of the details of a particular product, and hence fail to anticipate seasonal surges in customer demand in the lead up to major exogenous events like holidays for clearly relevant products. To address this shortcoming, this paper introduces a novel forecast post-processor -- which we call LLMForecaster -- that fine-tunes large language models (LLMs) to incorporate unstructured semantic and contextual information and historical data to improve the forecasts from an existing demand forecasting pipeline. In an industry-scale retail application, we demonstrate that our technique yields statistically significantly forecast improvements across several sets of products subject to holiday-driven demand surges.
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