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README.md

@@ -1,3 +1,56 @@
 ---
-license: mit
+license: other
+license_name: us-public-domain
+license_link: https://www.usa.gov/government-works
+pretty_name: "HPEC 2026 — HS6 US Trade Demand-Forecasting Lattice"
+tags:
+- time-series-forecasting
+- demand-forecasting
+- trade
+- census
+size_categories:
+- 10K<n<100K
 ---
+
+# HPEC 2026 — HS6 US Trade Demand-Forecasting Lattice
+
+Dataset for the IEEE HPEC 2026 paper **"Are Multidimensional Models Worth it in Demand Forecasting?"** (Paper ID 372). Built from the U.S. Census Bureau's bulk port-level HS6 merchandise trade files (public domain), it provides a dense commodity × state × flow lattice for benchmarking flat vs. explicitly multidimensional sequence models (RNNs, Transformers, S4/S4ND, Mamba-2/3, CaFA-style factorized attention).
+
+Code (models, training sweeps, lattice builder): https://github.com/triadastra/Multidimensional-Long-Series-Forecasting
+
+## Contents
+
+```
+raw/                          384 Census bulk ZIPs — port-level HS6 exports + imports, 2010–2025
+raw/reference/                Census codebooks and fixed-width record layouts
+processed/census_lattice_9ch.npz    main benchmark lattice (9 channels, ~219 MB)
+processed/census_lattice_9ch.json   sidecar metadata (REQUIRED — see below)
+processed/census_lattice.npz        earlier 2-channel build (~64 MB)
+processed/census_lattice.json       its sidecar
+```
+
+## Processed lattice (`census_lattice_9ch.npz`)
+
+- **Grid:** 1,263 HS6 commodities × 14 port-states × 2 flows (export/import), 192 monthly steps (2010–2025); cells kept at ≥80% density → **28,292 active series**.
+- **Channels (9):** aggregate + air/containerized/bulk/land **value**, and aggregate + air/containerized/bulk **weight**; all `log1p` then per-series MinMax fit on the training split only.
+- **Sidecar JSON is load-bearing:** the `.npz` carries no metadata. Target channels, axis labels, and split boundaries live in the matching `.json` — always load it alongside (`target_channels = [0, 5]` for the 9-channel build; a script reading the npz alone will silently pick wrong defaults).
+- Built by `scripts/build_census_lattice.py` in the GitHub repo (HS4 dev variant via `--level 4`).
+
+## Raw files
+
+230-character fixed-width records: HS6 (Schedule B) + country (Schedule C) + district/port (Schedule D → state) + YYYYMM + value/weight fields. Schedule B / HS codes were re-versioned in 2012, 2017, and 2022 — match each year to its vintage code list (layouts and vintage code tables in `raw/reference/`).
+
+## License & provenance
+
+Source data is produced by the U.S. Census Bureau and is in the **public domain** (17 U.S.C. § 105). This repackaging adds no restrictions.
+
+## Citation
+
+```bibtex
+@inproceedings{fan2026multidim,
+  title     = {Are Multidimensional Models Worth it in Demand Forecasting?},
+  author    = {Fan, Cheng-Jui and Aristov, Nikolay and Dugundji, Elenna},
+  booktitle = {IEEE High Performance Extreme Computing Conference (HPEC)},
+  year      = {2026}
+}
+```