Time Series Foundation Model

Time series foundation models aim to create general-purpose models capable of handling diverse time series tasks, such as forecasting and anomaly detection, across various domains without extensive task-specific retraining. Current research emphasizes scaling model size using techniques like Mixture-of-Experts (MoE) to improve accuracy and efficiency, exploring novel architectures such as transformers and MLP-Mixers, and developing effective pre-training and fine-tuning strategies including contrastive learning and parameter-efficient methods like LoRA. These advancements hold significant promise for improving the accuracy and efficiency of time series analysis across numerous scientific fields and practical applications, particularly in areas with limited labeled data.