基于Cold Causal需求预测模型缓解冷启动预测问题
Mitigating Cold-start Forecasting using Cold Causal Demand Forecasting Model
AI总结：
针对深度学习多变量时间序列预测模型因果关系捕捉不足、无法应对冷启动的问题，提出融合因果推断与深度学习的CDF-cold框架，在15个谷歌数据中心流量数据集上表现优于现有最优模型。
摘要：
多变量时间序列数据预测指利用历史数据预测变量随时间变化的未来值，具备重要的实际应用价值。尽管基于深度学习的模型已在该领域展现出应用潜力，但它们往往难以捕捉因变量之间的因果关系，导致预测精度不足。此外，这类模型无法应对时间序列数据中的冷启动问题——即部分变量缺乏历史数据，给识别变量间依赖关系带来了挑战。为解决上述局限，我们提出Cold Causal Demand Forecasting（CDF-cold，冷因果需求预测）框架，该框架将因果推断与基于深度学习的模型相结合，以提升受冷启动问题影响的多变量时间序列数据的预测精度。为验证所提方法的有效性，我们收集了15个包含不同Google数据中心网络流量的多变量时间序列数据集。实验结果表明，在预测多变量时间序列数据的未来值方面，CDF-cold框架的表现优于现有最先进的预测模型。
英文摘要：
Forecasting multivariate time series data, which involves predicting future values of variables over time using historical data, has significant practical applications. Although deep learning-based models have shown promise in this field, they often fail to capture the causal relationship between dependent variables, leading to less accurate forecasts. Additionally, these models cannot handle the cold-start problem in time series data, where certain variables lack historical data, posing challenges in identifying dependencies among variables. To address these limitations, we introduce the Cold Causal Demand Forecasting (CDF-cold) framework that integrates causal inference with deep learning-based models to enhance the forecasting accuracy of multivariate time series data affected by the cold-start problem. To validate the effectiveness of the proposed approach, we collect 15 multivariate time-series datasets containing the network traffic of different Google data centers. Our experiments demonstrate that the CDF-cold framework outperforms state-of-the-art forecasting models in predicting future values of multivariate time series data.
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