Cadence uses a time-series foundation model for error-bounded lossy compression of demand data
Roberto Tacconelli · hf · 2026-09-09
Cadence pairs a time-series foundation model with an adaptive arithmetic coder for error-bounded lossy compression of demand time series, substantially outperforming classical predictors.
The authors also report negative results: the method does not work for lossless coding or under cross-batch determinism requirements, clarifying its applicability boundaries.
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