# Forecasting trends in food security with real time data

arXiv: https://arxiv.org/abs/2312.00626 · HTML: https://mlsanity.com/paper/2312.00626/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 65 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics](https://mlsanity.com/paper/1910.05266/) (2019)
- [Universal features of price formation in financial markets: perspectives from Deep Learning](https://mlsanity.com/paper/1803.06917/) (2018)
- [Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers](https://mlsanity.com/paper/1910.00659/) (2019)
- [Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices](https://mlsanity.com/paper/2107.12770/) (2021)

## Built on by (most cited first, 6 of 6)

- [A monthly sub-national Harmonized Food Insecurity Dataset for comprehensive analysis and predictive modeling](https://mlsanity.com/paper/2501.06076/) (2025)
- [Controlling Dynamical Systems into Unseen Target States Using Machine Learning](https://mlsanity.com/paper/2412.10251/) (2024)
- [Tailored minimal reservoir computing: on the bidirectional connection between nonlinearities in the reservoir and in data](https://mlsanity.com/paper/2504.17503/) (2025)
- [A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset](https://mlsanity.com/paper/2604.06227/) (2026)
- [Distinguishing indistinguishable attractors: Unsupervised anomaly detection with reservoir computers](https://mlsanity.com/paper/2606.21322/) (2026)
- [Climate Variability Modulates the Impact of Price Spikes on Food Insecurity](https://mlsanity.com/paper/2609.24394/) (2026)

## Abstract (arXiv)

Early warning systems are an essential tool for effective humanitarian action. Advance warnings on impending disasters facilitate timely and targeted response which help save lives and livelihoods. In this work we present a quantitative methodology to forecast levels of food consumption for 60 consecutive days, at the sub-national level, in four countries: Mali, Nigeria, Syria, and Yemen. The methodology is built on publicly available data from the World Food Programme's global hunger monitoring system which collects, processes, and displays daily updates on key food security metrics, conflict, weather events, and other drivers of food insecurity. In this study we assessed the performance of various models including Autoregressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost), Long Short Term Memory (LSTM) Network, Convolutional Neural Network (CNN), and Reservoir Computing (RC), by comparing their Root Mean Squared Error (RMSE) metrics. Our findings highlight Reservoir Computing as a particularly well-suited model in the field of food security given both its notable resistance to over-fitting on limited data samples and its efficient training capabilities. The methodology we introduce establishes the groundwork for a global, data-driven early warning system designed to anticipate and detect food insecurity.