AI 中文总结
研究比较ARIMA、VAR、随机森林、XGBoost、LSTM和GRU六种模型对加拿大月度通胀的预测能力，采用扩展窗口向前验证，发现预测期不同模型表现有差异，XGBoost表现最佳，SHAP分析显示滞后通胀影响更大，明确机器学习何时超越传统基准。
AI 中文摘要
本研究评估了六种模型类型（ARIMA、VAR、随机森林、XGBoost、LSTM和GRU）对2012年1月至2026年4月加拿大月度通胀（n = 172）的样本外预测能力。采用扩展窗口向前验证，跨越1、3、6和12个月的预测期。结果显示存在预测期依赖的变化：在1个月预测期ARIMA显著优于所有机器学习和深度学习模型；但在6个月和12个月时，随机森林和XGBoost表现更优，与ARIMA和VAR相比RMSE降低约30%-75%。LSTM和GRU仅在最短预测期表现良好。分析四个宏观经济子时期表明没有单一模型始终占优。对表现最佳的XGBoost模型的SHAP分析表明滞后通胀比失业更具影响力，失业仅在疫情后期有显著影响。研究结果明确了机器学习方法何时能超越传统基准。
英文摘要
This study evaluates the out-of-sample forecasting ability of six model types: ARIMA, VAR, Random Forest, XGBoost, LSTM, and GRU, for monthly Canadian inflation from January 2012 to April 2026 (n = 172). The evaluation employs expanding-window walk-forward validation across 1-, 3-, 6-, and 12-month horizons. Results reveal a horizon-dependent shift: ARIMA significantly outperforms all machine learning and deep learning models at the one-month horizon (Diebold-Mariano p < 0.05). However, Random Forest and XGBoost become notably superior at six and twelve months, reducing RMSE by approximately 30-75 percent compared to ARIMA and VAR. LSTM and GRU perform well only at the shortest horizon, likely due to overfitting given the limited data. Analyzing four macroeconomic sub-periods shows that no single model consistently dominates. SHAP analysis of the top-performing XGBoost model indicates that lagged inflation is more influential than unemployment, which only becomes significantly impactful during the pandemic tail. The findings clarify when machine learning methods can surpass traditional benchmarks.
