Synthesizing time series data is pivotal in modern society, aiding effective decision-making and ensuring privacy preservation in various scenarios. Time series are influenced by both intrinsic factors, such as observable attributes, and extrinsic factors, including external environmental settings, which makes it challenging for time series generation.Recent research has predominantly focused on random unconditional time series synthesis or conditional synthesis based on specific information. Nonetheless, generating time series from scratch may confront with unexpected outcomes or inadvertently prioritize trivial attributes while neglecting crucial ones.This paper proposes a novel approach to efficiently generate target time series through Time Series Editing (TSE) upon existing samples. The objective is to modify the given time series according to the specified properties while preserving other attributes unchanged.This task is not trivial due to the intricate relationships between time series and their corresponding attributes.We introduce a novel multi-resolution modeling and generation paradigm, incorporating bootstrap tuning on self-generated instances to enhance the coverage of the original data.Our experiments demonstrate the efficacy of our method and its potential for directly modifying specified time series attributes upon the existing time series data.