This document presents CTF, a coarse-to-fine model transfer framework for anomaly detection in high-dimensional time series data. CTF first clusters time series data into groups using a distribution of latent features, then trains an RNN-VAE model for each cluster with fine-tuning. This allows scalable training and achieves better performance than alternatives. An evaluation on a large real-world dataset showed CTF improved F1 score from 0.830 to 0.892 while maintaining scalability. Design choices for clustering objects, distance measures, and algorithms were also validated.