TY - GEN

T1 - Time-universal data compression and prediction

AU - Ryabko, Boris

PY - 2019/7/1

Y1 - 2019/7/1

N2 - Suppose there is a large file which should be transmitted (or stored) and there are several (say, m) admissible data-compressors. It seems natural to try all the compressors and then choose the best, i.e. the one that gives the shortest compressed file. Then transfer (or store) the index number of the best compressor (it requires [log m] bits) the compressed file. The only problem is the time, which essentially increases due to the need to compress the file m times (in order to find the best compressor). We propose a method that encodes the file with the optimal compressor, but uses a relatively small additional time: the ratio of this extra time and the total time of calculation can be limited by an arbitrary positive constant. A similar situation occurs when forecasting time series.Generally speaking, in many situations it may be necessary find the best data compressor (or predictor) out of a given set, which is often done by comparing them empirically. One of the goals of this work is to turn such a selection process into a part of the data compression method, automating and optimizing it.

AB - Suppose there is a large file which should be transmitted (or stored) and there are several (say, m) admissible data-compressors. It seems natural to try all the compressors and then choose the best, i.e. the one that gives the shortest compressed file. Then transfer (or store) the index number of the best compressor (it requires [log m] bits) the compressed file. The only problem is the time, which essentially increases due to the need to compress the file m times (in order to find the best compressor). We propose a method that encodes the file with the optimal compressor, but uses a relatively small additional time: the ratio of this extra time and the total time of calculation can be limited by an arbitrary positive constant. A similar situation occurs when forecasting time series.Generally speaking, in many situations it may be necessary find the best data compressor (or predictor) out of a given set, which is often done by comparing them empirically. One of the goals of this work is to turn such a selection process into a part of the data compression method, automating and optimizing it.

UR - http://www.scopus.com/inward/record.url?scp=85073171866&partnerID=8YFLogxK

U2 - 10.1109/ISIT.2019.8849224

DO - 10.1109/ISIT.2019.8849224

M3 - Conference contribution

AN - SCOPUS:85073171866

T3 - IEEE International Symposium on Information Theory - Proceedings

SP - 562

EP - 566

BT - 2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Symposium on Information Theory, ISIT 2019

Y2 - 7 July 2019 through 12 July 2019

ER -