| --- |
| 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} |
| } |
| ``` |
|
|
