This article presents a data-driven probabilistic framework for estimating missing values in multivariate and time-varying interdependent IoT data streams during reefer container monitoring. It models the periodic fluctuations in the temperature and humidity due to the refrigeration cycle using a log-normal distribution, followed by the estimation of missing values using the sparse vector autoregression (sVAR) model. The accuracy of sVAR is improved by considering the spatio-temporal correlation of sensor signals while computing the model parameters and a kernel-based weighting scheme. It is applied to a dataset collected during an experiment. The results show that it outperforms a few baseline methods while providing a comprehensive solution for both point-missing values as well as large gap situations considering co-occurring and non-co-occurring cases.