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Multivariate time series forecasting plays a pivotal role in contemporary webtechnologies. In contrast to conventional methods that involve creatingdedicated models for specific time series application domains, this researchadvocates for a unified model paradigm that transcends domain boundaries.However, learning an effective cross-domain model presents the followingchallenges. First, various domains exhibit disparities in data characteristics,e.g., the number of variables, posing hurdles for existing models that imposeinflexible constraints on these factors. Second, the model may encounterdifficulties in distinguishing data from various domains, leading to suboptimalperformance in our assessments. Third, the diverse convergence rates of timeseries domains can also result in compromised empirical performance. To addressthese issues, we propose UniTime for effective cross-domain time serieslearning. Concretely, UniTime can flexibly adapt to data with varyingcharacteristics. It also uses domain instructions and a Language-TS Transformerto 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 advancingstate-of-the-art forecasting performance and zero-shot transferability.

UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting