The results JSON from the Colab run, with its provenance recorded in the
file: A100, 12 rungs, 3 assets, 5 purged folds, 15 epochs, seq_len 48,
30,969 rows, repo commit 7b0fceb. run_ablation_study.py regenerates it.
fig2 now draws all twelve rungs across all three assets, as margin over the
majority class rather than accuracy -- accuracy alone puts the naive rungs
at a respectable-looking 50% and hides that they are the floor. A shaded
band marks the range the nine non-linear architectures occupy, 20.6 to 24.2
points, which is the figure's argument: they are indistinguishable from one
another, and the step that matters is the one to their left.
Two small fixes found while drawing it: the figure read the first key of the
results file as an asset, which a leading provenance note broke, and the
target-screen figure needed the same guard.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Add the ablation, HAR benchmark, figures and three related-work comparisons
Fifteen studies in three groups -- volatility benchmarking, architecture
ablation, historic versus sentiment -- kept separate because a paper
benchmarking against HAR-RV is not evidence about whether a VAE earns its
place, and mixing them is how a related-work chapter ends up comparing
numbers that were never comparable.
run_har_benchmark.py regresses log realised volatility against HAR-RV on
identical folds and reports QLIKE as well as R2, which is what makes the
comparison like-for-like. Nine cells, three assets, three horizons: the
neural network wins two, both on BTC, and loses QLIKE in both. On ETH and
ADA ordinary least squares on the same 33 features wins both metrics at
every horizon. Improvement over HAR is 1.1-1.4x against the 5.4x claimed by
arXiv 2511.20105, whose HAR scores 0.128 where this one scores 0.32-0.36.
It also corrects an earlier figure in sota_volatility_comparison.md: the
2.1x reported there used a weak HAR specification taken from stored
metadata. Fitted pro…
