FOOD

World Food Security

Forecast Cycle 2026

Scientific Framework

Global Food Inflation:
Bi-Monthly Early Warning & Near-Term Forecast

The Dual-branch Prediction Model model consists of three parts: temporal T-branch, causal G-branch and attention fusion module. This framework targets 93 nations, identifying anomalies 2 months in advance.

Dual-branch Prediction Model Inflation Index

StableExtreme Risk
General Area

Global Risk Momentum Flow Chart

Regional Analysis: Key Drivers

Official Resources LIVE DATA

EconomicJAN 15, 2026
UN DESA: World Economic Situation 2026 Report
Real-time LIVE MONITOR
WFP: Global Hunger Crisis Risk Monitoring Map
Policy IMF BLOG
IMF: World Economic Outlook 2026
Trade MONTHLY UPDATES
World Bank: Food Security Update & Commodity Prices
Index FAO STAT
FAO: World Food Price Index (Monthly Time-Series)
Satellite GEO DATA
GEOGLAM: Global Agricultural Monitoring Dashboard

Dual-branch Prediction

01. T-Branch

The T-branch applies instance normalization and uses a moving average (kernel size 5) to decompose trend and seasonal components. These are linearly fused and processed through a two-layer PastDecomposableMixing module to extract temporal inflation patterns, output via a denormalized prediction head.

02. G-Branch

The G-branch fuses immediate, lagged, and differential views into an asymmetric adjacency matrix. These relationships are processed through normalized residual layers with GELU and dropout to capture spatial interactions, outputting the inflation forecast via a two-layer fully-connected head.

03. Attention Fusion

The outputs of T-branch and G-branch are mapped to the unified latent space respectively, and the two branches are adaptively weighted by the learnable query vector through the scaled dot product attention mechanism, and the final prediction result is obtained by output projection.

Dual-branch Prediction Model Architecture

Dual-branch Prediction Model Architecture

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Model Source Code

Download the core implementation of the Dual-branch Prediction Model. Includes T-branch temporal consistency modules, G-branch multi-view causal graph convolutional layers, and the adaptive attention-based fusion weights script.

Download .PY Script

Regional Strategic Reports

Country

Detailed Risk Assessment 2026

AWLM Forecast

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Country Profile

MONITORING

Pearson Correlation

SHAP Feature Importance

Characteristics

Raw Data Terminal

Access 93 Nations Historical & Forecast datasets

Nation ID Indicator Jan Forecast Feb Forecast Confidence Interval Action