It is a common issue in time series to encounter missing data， which complicates subsequent time series analysis. Effective missing value imputation is crucial for improving data quality and mining data value. However， attention modules designed for complete data in time series prediction tasks are often used in the existing imputation algorithms， which are insufficient for extracting spatio-temporal features from time series with missing values. Additionally， it is rare for the existing imputation algorithms to perform in-depth research on imputation patterns， as they underestimate the intermediate values generated during imputation process， so that there is still room for improvement in the accuracy of the imputation. In view of the above problems， an Incremental missing value Imputation algorithm for Time series based on Diffusion Model （I2TDM） was proposed. In I2TDM， to enhance the feature extraction capabilities for time series with missing values， a temporal attention module was incorporated into the traditional diffusion model. At the same time， to improve stability and accuracy of the imputation algorithm， a novel incremental imputation algorithm was proposed to use the incremental selection module to retain partial intermediate imputation values. Experimental results of imputation experiments on 3 datasets — Air Quality Index （AQI）， Electricity Transformer Temperature （ETT） and Weather show that compared with baseline models such as CSDI， SAITS， and PriSTI， the I2TDM achieves a reduction of at least 2.92% in the Mean Absolute Error （MAE） metric and at least 3.49% in the Root Mean Square Error （RMSE） metric， which demonstrates the effectiveness of I2TDM in improving missing value imputation accuracy of time series.