README.md

metadata

license: apache-2.0
pretty_name: OpenRelief — Food-Security Forecasting Dataset
task_categories:
  - time-series-forecasting
  - text-generation
tags:
  - food-security
  - ipc
  - famine-early-warning
  - time-series
  - humanitarian
  - llm-annotation
language:
  - en
size_categories:
  - 10K<n<100K

OpenRelief

A retrospective research dataset and benchmark pairing district-level food-security histories with national context, built to study FEWS NET IPC phase forecasting and time-series-to-language supervision — teaching a language model to connect historical signals into a written, evidence-cited rationale rather than just a number.

Built for the Temporal AI Challenge. Source code, frontend and full documentation: github.com/luk-huebner/OpenRelief.

Task

Given six months of history across 21 channels (food-security indices, prior IPC assessments, shipping, conflict, rainfall, staple prices), predict the FEWS NET IPC phase (1 Minimal → 5 Famine) three calendar months ahead, with a generated rationale, cross-domain hypotheses and recommended actions.

Dataset at a glance

Field	Value
Dataset version	8142c89de89cb862117d6a814be515b9f00312bb960e33cf7e994a99ed124bd3
Train	9,065 examples / 16 countries (cutoffs through Dec 2022)
Validation	2,503 examples / 16 countries (Jan–Jul 2023 cutoffs)
Test	2,230 examples / 14 countries (Aug–Dec 2023 cutoffs, target exactly +3 months)
Channels	21 (six monthly observations each, plus observed-value masks)
Test label support	Phase 1: 854 · Phase 2: 976 · Phase 3: 395 · Phase 4: 5 · Phase 5: 0
Annotation coverage	2,781 / 9,065 training records (30.7%) — partial, not complete

Prepared arrays live under artifacts/dataset/ and artifacts/multimodal/ (inputs.jsonl, targets.jsonl, manifest.json). Loaders verify dataset checksums against the version above before use.

Sources

• HFID — district panel: FEWS NET IPC labels, normalized FCS/rCSI.
• IMF PortWatch — national shipping/port-call volumes.
• ACLED — national conflict events and fatalities.
• CHIRPS — country-average rainfall.
• WFP — national staple-food price series, with commodity/unit metadata.

National covariates are aggregated to country level; only FCS/rCSI and the IPC label are at district (admin2) resolution. The benchmark assumes one-month release lags for HFID/PortWatch/ACLED/WFP and two months for CHIRPS — it does not establish what could actually have been forecast in real time. Full provenance: SOURCES.md, DATASET_CARD.md.

Annotations

gpt-5.6-terra generated structured training supervision for 2,781 of the 9,065 training examples: precursor claims (with channel citations), cross-domain hypotheses, a written rationale, driver-cited recommended actions, and an uncertainty/confidence note. The teacher was given the true future IPC phase — this is retrospective supervision, not independent forecasting reasoning, and hypotheses are not proven causes. Unannotated training examples train on an empty rationale/action target (phase-only supervision).

Three fully worked examples — one deterioration, one persistent crisis, one improvement, each with its input charts, generated hypotheses, verbatim rationale and recommended actions — are in the annotation atlas (exact examples: docs/examples/annotation-atlas/examples.json). It is an editorial illustration of three selected cases, not a representative quality sample.

Model results (this repo's gpu-results/)

OpenTSLM/llama-3.2-1b-tsqa-sp (Llama 3.2 1B backbone), fine-tuned with LoRA (rank 16, alpha 32) on the 2,781 annotated training examples.

Fine-tuning is not optional. The untouched pretrained checkpoint, evaluated on the full 2,230-example test set with no LoRA at all, produced 0% valid JSON output — it never attempts the task's output format. See gpu-results/pretrained-fulltest/.

Source ablation (256-example eval cohort, one source's channels dropped at a time — see caveat below):

Run	Macro-F1	Accuracy
All sources (baseline)	0.7199	0.7344
Drop conflict (ACLED)	0.7428	0.7617
Drop rainfall (CHIRPS)	0.7286	0.7500
Drop prices (WFP)	0.7300	0.7500
Drop shipping (PortWatch)	0.5826	0.7852

Dropping PortWatch (shipping) is the only ablation that meaningfully hurts the model — a 0.14 macro-F1 drop, far outside the noise band of the other three. Full writeup: FINDING-portwatch-signal.md.

Caveat: the ablation table above is a 256-example cohort with only one phase-4 example and no phase-5 examples in the full test set at all — do not read this as demonstrated famine prediction. The clearer, full-test-set wins are the pretrained-vs-fine-tuned gap above and the PortWatch ablation signal; match sample IDs before comparing across cohorts. See STATUS.md for the full, currently-known comparison table.

The fine-tuned LoRA adapter (gpu-results/all-sources/best_model.pt, SHA-256 a10343caa152d1c3aa55b6dc9b40e11603067babeac44909001d1597a3e84e10) is deployed as a live inference endpoint that powers the project's demo frontend.

Repository layout

artifacts/dataset/        prepared inputs.jsonl / targets.jsonl / manifest.json
artifacts/multimodal/     multimodal-formatted version of the same splits
artifacts/annotation-cache/  cached per-example teacher annotation responses
artifacts/*.manifest.json    acquisition manifests per source connector
gpu-results/               fine-tuning + ablation runs (see table above)
  all-sources/               baseline checkpoint, losses, benchmark, best_model.pt
  ablation-{acled,chirps,wfp,portwatch}/   one-source-dropped reruns
  pretrained-fulltest/        untouched-checkpoint baseline, full test set
  manifest.json                git commit + image digest + launch command per run
refrences/                 background reading + one raw source CSV

Limitations

• National-level signals (shipping, conflict, rainfall, price) do not prove district-level exposure; only FCS/rCSI and the IPC label are district-resolution.
• FCS/rCSI normalization direction is undocumented — numeric movement is not given a severity interpretation.
• No phase-5 test examples exist and only five phase-4 examples — this dataset does not support a claim of demonstrated famine prediction.
• Ablation effects are associative, measured against removed input channels, not causal claims.
• Historical publication timestamps and revisions are unavailable; assumed release lags are a modeling choice, not an observed fact.

License and reuse

Repository code is Apache-2.0. Access to an upstream source here does not imply unrestricted redistribution rights over that source's original data — retain IMF PortWatch terms, ACLED terms of use, WFP source metadata, CHIRPS attribution and geoBoundaries attribution as applicable to each channel. See DATASET_CARD.md for the full statement.