FOOD

World Food Security

Forecast Cycle 2026

Scientific Framework

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

The AWLM (Adaptive Wavelet-Linear MixNet) 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.

AWLM 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

AWLM: Adaptive Wavelet-Linear MixNet

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

Regional Strategic Reports

Raw Data Terminal

Access 88 Nations Historical & Forecast datasets

Nation ID Indicator Jan Forecast Feb Forecast Confidence Interval Action