Pre-trained time series models are machine learning models that are trained on large volumes of sequential data to learn general temporal patterns and representations before being adapted to specific downstream applications. Instead of requiring a specialized architecture to be trained from scratch on an isolated dataset, these models leverage broad pre-training across diverse time-oriented data to capture universal temporal dynamics such as seasonality, trends, and cross-variable dependencies. Once pre-trained, they can be deployed directly in zero-shot settings or fine-tuned on limited target data to perform various analytical tasks, including time series forecasting, anomaly detection, classification, and missing value imputation across diverse domains.
