  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.
