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Optimal text-based time-series indices

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  • Ardia, David
  • Bluteau, Keven

Abstract

We propose an approach to construct text-based time-series indices in an optimal way—typically, indices that maximize the contemporaneous relation or the predictive performance with respect to a target variable, such as inflation. Our methodology relies on binary selection matrices that, applied to the vocabulary of tokens, select the relevant texts in the corpus. Various widely known text-based indices, such as the Economic Policy Uncertainty (EPU) index, can be formulated in terms of selection matrices. We design a genetic algorithm with domain-specific knowledge featuring tailor-made crossover and mutation operations to perform the complex optimization. We illustrate our methodology with a corpus of news articles from the Wall Street Journal by optimizing text-based indices that forecast inflation at various horizons.

Suggested Citation

  • Ardia, David & Bluteau, Keven, 2026. "Optimal text-based time-series indices," International Journal of Forecasting, Elsevier, vol. 42(1), pages 44-60.
    • Handle: RePEc:eee:intfor:v:42:y:2026:i:1:p:44-60
    DOI: 10.1016/j.ijforecast.2025.07.003
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