用大模型提升美国人口预测精度，尤其擅长少数群体稀疏数据
Comparative Analysis of Time Series Foundation Models for Demographic Forecasting: Enhancing Predictive Accuracy in US Population Dynamics
- 用预训练时序基础模型TimesFM替代传统方法
- 在6个州的预测中86.67%情况误差最低，少数群体表现更优
- 无需大量调参，适合政策制定者快速部署
人口变迁受全球化、经济形势、地缘政治和环境因素影响，对政策制定者与研究者构成重大挑战。准确的人口预测对城市规划、医疗资源配置和经济政策制定至关重要。本研究利用美国人口普查局和联邦储备经济数据（FRED）的时序数据，评估时间序列基础模型（TimesFM）在预测美国人口动态中的表现，对比了长短期记忆网络（LSTM）、自回归积分滑动平均模型（ARIMA）和线性回归等传统基线方法。在六个具有人口多样性特征的州进行实验，结果显示：在86.67%的测试案例中，TimesFM的均方误差（MSE）最低，尤其在历史数据稀疏的少数族裔群体上表现突出。这些发现表明，预训练基础模型可显著提升人口分析能力，支持无需大量微调的主动式政策干预。
原文摘要 · Abstract (English)
Demographic shifts, influenced by globalization, economic conditions, geopolitical events, and environmental factors, pose significant challenges for policymakers and researchers. Accurate demographic forecasting is essential for informed decision-making in areas such as urban planning, healthcare, and economic policy. This study explores the application of time series foundation models to predict demographic changes in the United States using datasets from the U.S. Census Bureau and Federal Reserve Economic Data (FRED). We evaluate the performance of the Time Series Foundation Model (TimesFM) against traditional baselines including Long Short-Term Memory (LSTM) networks, Autoregressive Integrated Moving Average (ARIMA), and Linear Regression. Our experiments across six demographically diverse states demonstrate that TimesFM achieves the lowest Mean Squared Error (MSE) in 86.67% of test cases, with particularly strong performance on minority populations with sparse historical data. These findings highlight the potential of pre-trained foundation models to enhance demographic analysis and inform proactive policy interventions without requiring extensive task-specific fine-tuning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。