From Seasonality to Semantics: Benchmarking a Hybrid Probabilistic Forecasting System for Roadblocks in Bolivia

2026-07-27 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Social Sciences & Behavioral Studies · Depth: Expert, extended

Summary

A hybrid probabilistic forecasting system for roadblocks in Bolivia integrates time series decomposition (Prophet) with natural language processing (NLP) techniques, specifically vector semantic embeddings and zero-shot classification, applied to a six-year corpus of Bolivian news. The system, evaluated over 1,762 days and seven forecasting horizons (H+1 to H+7) against seven internal configurations and four external benchmarks (including SARIMA and LightGBM), demonstrates that the hybrid configuration (Prophet + NLP, C6) consistently outperforms purely statistical models. C6 achieved an AUC-ROC of 0.677 at H+1 and reduced the Brier Score by 10.9 % relative to the baseline temporal model (0.220 vs. 0.247), maintaining a statistically significant error reduction ($p<0{,}02$) across all horizons. This research validates that integrating semantic news signals detects social tension peaks not captured by historical data, offering a technical tool for risk management in critical transport corridors.

Key takeaway

For Machine Learning Engineers developing predictive systems for social unrest or supply chain risk, you should integrate time series models like Prophet with natural language processing of local news. This hybrid approach, specifically using semantic embeddings, significantly improves forecast accuracy and probabilistic calibration over purely statistical or NLP-only models, especially for medium-term horizons (H+3 onwards). Ensure your models are well-calibrated to enable effective decision threshold management for operational alerts.

Key insights

Hybrid models combining time series and NLP significantly improve social conflict forecasting over traditional methods.

Principles

• Semantic signals enhance seasonal models.
• Probabilistic calibration is key for operational use.
• Pure NLP models lack temporal anchoring.

Method

A hybrid probabilistic forecasting system integrates Prophet for seasonal decomposition with NLP (vector semantic embeddings, zero-shot classification) on news headlines, using XGBoost and walk-forward validation.

In practice

• Combine time series with news embeddings.
• Prioritize models with strong probabilistic calibration.
• Use zero-shot classification for thematic filtering.

Topics

• Probabilistic Forecasting
• Natural Language Processing
• Time Series Forecasting
• Social Conflict Prediction
• Hybrid Machine Learning
• Bolivia

Best for: NLP Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.