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 captures details via Wavelet decomposition and overall trends with a parallel DLinear module. This framework targets 93 nations, identifying anomalies 2 months in advance.

Dual-branch Prediction Model Inflation Index

StableExtreme Risk
General Area

Emerging Markets Trends

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: Resilience in the Global Economy 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. Wavelet Decomposition

Complex non-stationary inflation time series are decomposed via Discrete Wavelet Transform (DWT) into High-frequency Noise and Low-frequency Trend components.

02. DLinear Parallel Module

Utilizing a DLinear architecture to capture long-term sequence trends directly through single-layer linear networks, significantly reducing computational overhead compared to Transformers.

Architecture Flowchart

Technical Pipeline: Input Data DWT DLinear Fusion Output

Model Source Code

Download the core implementation of the Dual-branch Prediction Model. Includes Wavelet decomposition layers, DLinear trend modules, and the adaptive 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