When processing huge data streams in information systems, individual measurements or whole groups of measurements can be distorted or lost due to various reasons. Recovery of compressed data during transmission on communication channels is accompanied by errors related to distortion of information and service parts of messages due to presence of interference in transmission channel. To these errors are added errors caused by quantization of the transmitted implementations by level and time sampling. Research on methods of increasing noise immunity both during transmission and during recovery of measured data is an urgent task in the design of information and measurement systems.

The article considers non-parametric methods of estimating probabilistic characteristics of random processes. A distinctive feature of non-parametric methods is the ranking of data measured at the observation interval. It is shown that ranking of data on transmitting side of information-measuring system enables correction of errors and failures based on strict monotony of ranked number of codes. Also, the error of recovery of continuous implementations taking into account distortions of compressed data in the communication channel was investigated. The obtained results indicate that the use of complex compression algorithms is impractical, since the difference in the error in the restoration of non-stationary messages between the simplest algorithm and the rather difficult one becomes negligible. The article presents the results of estimating recovery errors for various data compression methods.