Abstract: Incomplete big data, due to its irregular structure, has a large amount of computation and low interpolation accuracy when interpolation misses values. Therefore, a missing value interpolation algorithm for incomplete big data based on spatial autoregressive model is proposed. Using a migration learning algorithm to filter out redundant data from the original data under dynamic weights, to distinguish abnormal data from normal data, and to extract incomplete data. Using least square regression to repair the incomplete data. The missing value interpolation is divided into three types, namely, first order spatial autoregressive model interpolation, spatial autoregressive model interpolation, and multiple interpolation. The repaired data is interpolated to the appropriate location according to the actual situation, implementing incomplete big data missing value interpolation. Experimental results show that the proposed method has good interpolation ability for missing values. 

Key words: transfer learning, incomplete big data, imputation of missing values, spatial regression model, data correction