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 88 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 Projection -> Fusion Output

Model Source Code

Download the core implementation of the Dual-branch Prediction Model. The package includes the Wavelet decomposition layers, DLinear trend modules, and the adaptive fusion weights script (PyTorch version).

Download .PY Script

Regional Strategic Reports

Raw Data Terminal

Access 93 Nations Historical & Forecast datasets

Nation ID Indicator Jan Forecast Feb Forecast Confidence Interval Action