# EMBERS at 4 years: Experiences operating an Open Source Indicators Forecasting System

arXiv: https://arxiv.org/abs/1604.00033 · HTML: https://mlsanity.com/paper/1604.00033/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2016.

## Built on by (most cited first, 4 of 4)

- [Deep Spatial Domain Generalization](https://mlsanity.com/paper/2210.00729/) (2022)
- [An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graph](https://mlsanity.com/paper/2312.02334/) (2023)
- [A Survey on Societal Event Forecasting with Deep Learning](https://mlsanity.com/paper/2112.06345/) (2021)
- [WikiCausal: Corpus and Evaluation Framework for Causal Knowledge Graph Construction](https://mlsanity.com/paper/2409.00331/) (2024)

## Abstract (arXiv)

EMBERS is an anticipatory intelligence system forecasting population-level events in multiple countries of Latin America. A deployed system from 2012, EMBERS has been generating alerts 24x7 by ingesting a broad range of data sources including news, blogs, tweets, machine coded events, currency rates, and food prices. In this paper, we describe our experiences operating EMBERS continuously for nearly 4 years, with specific attention to the discoveries it has enabled, correct as well as missed forecasts, and lessons learnt from participating in a forecasting tournament including our perspectives on the limits of forecasting and ethical considerations.