  Multivariate time series forecasting plays a pivotal role in contemporary web
technologies. In contrast to conventional methods that involve creating
dedicated models for specific time series application domains, this research
advocates for a unified model paradigm that transcends domain boundaries.
However, learning an effective cross-domain model presents the following
challenges. First, various domains exhibit disparities in data characteristics,
e.g., the number of variables, posing hurdles for existing models that impose
inflexible constraints on these factors. Second, the model may encounter
difficulties in distinguishing data from various domains, leading to suboptimal
performance in our assessments. Third, the diverse convergence rates of time
series domains can also result in compromised empirical performance. To address
these issues, we propose UniTime for effective cross-domain time series
learning. Concretely, UniTime can flexibly adapt to data with varying
characteristics. It also uses domain instructions and a Language-TS Transformer
to offer identification information and align two modalities. In addition,
UniTime employs masking to alleviate domain convergence speed imbalance issues.
Our extensive experiments demonstrate the effectiveness of UniTime in advancing
state-of-the-art forecasting performance and zero-shot transferability.
