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.

翻译：多变量时间序列数据的预测涉及利用历史数据预测变量随时间变化的未来值，具有重要的实际应用价值。尽管基于深度学习的模型在这一领域展现出潜力，但它们通常难以捕捉因变量之间的因果关系，从而导致预测精度不足。此外，这些模型无法处理时间序列数据中的冷启动问题——即某些变量缺乏历史数据，这给识别变量间的依赖关系带来了挑战。为解决这些局限性，我们提出了冷因果需求预测（CDF-cold）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准确性。为验证所提方法的有效性，我们收集了包含不同谷歌数据中心网络流量的15个多变量时间序列数据集。实验结果表明，CDF-cold框架在预测多变量时间序列数据的未来值方面优于现有最先进的预测模型。