alt Hacker NewsI’m not exactly sure what distinction you are trying to make, but a compressor that generalizes from currently observed day to potential future data _is_ a better compressor, as it will have better predictions as it observes more of the source data distribution. Maybe you are making a point about whether compressors can “learn” after compressing the input data, but this also isn’t a fundamental property of compressors. Also compressors do have to deal with non-stationary data sources, so it also isn’t correct to say a compressor only models stationary distributions.
But your definition of compression as prediction is tautological. How would you operationalize that into a measurable metric of compression?
The crucial idea is that once you do that and start optimizing for compression, at some point more compression on the training dataset can lead to worse predictions on the futures you care for. Which is very incompatible with the poster statement.