Multivariate time series data are pervasive in various domains, often plagued by missing values due to diverse reasons. Diffusion models have demonstrated their prowess for imputing missing values in time series by leveraging stochastic processes. Nonetheless, a persistent challenge surfaces when diffusion models encounter the task of accurately modeling time series data with quick changes. In response to this challenge, we present the Acceleration-guided Diffusion model for Multivariate time series Imputation (ADMI). Time-series representation learning is first effectively conducted through an acceleration-guided masked modeling framework. Subsequently, representations with a special care of quick changes are incorporated as guiding elements in the diffusion model, utilizing the cross-attention mechanism. Thus our model can self-adaptively adjust the weights associated with the representation during the denoising process. Our experiments, conducted on real-world datasets featuring genuine missing values, conclusively demonstrate the superior performance of our ADMI model. It excels in both imputation accuracy and the overall enhancement of downstream applications.