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Zero-Shot Dengue Forecasting with LLM-based Time Series Foundation Models

by Pratik S. Machchar, Purvi N. Ramanuj
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 141
Year of Publication: 2026
Authors: Pratik S. Machchar, Purvi N. Ramanuj
10.5120/ijcab17db3823872

Pratik S. Machchar, Purvi N. Ramanuj . Zero-Shot Dengue Forecasting with LLM-based Time Series Foundation Models. International Journal of Computer Applications. 187, 141 ( Sep 2026), 31-38. DOI=10.5120/ijcab17db3823872

@article{ 10.5120/ijcab17db3823872,
author = { Pratik S. Machchar, Purvi N. Ramanuj },
title = { Zero-Shot Dengue Forecasting with LLM-based Time Series Foundation Models },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2026 },
volume = { 187 },
number = { 141 },
month = { Sep },
year = { 2026 },
issn = { 0975-8887 },
pages = { 31-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number141/zero-shot-dengue-forecasting-with-llm-based-time-series-foundation-models/ },
doi = { 10.5120/ijcab17db3823872 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-09-19T02:57:21.991903+05:30
%A Pratik S. Machchar
%A Purvi N. Ramanuj
%T Zero-Shot Dengue Forecasting with LLM-based Time Series Foundation Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 141
%P 31-38
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Worldwide, approximately 390 million dengue infections occur annually. Health systems in the tropics struggle to prepare for outbreaks, even when there is forewarning of impending surges that threaten to overrun hospitals. This paper explores whether recent large language model (LLM)-based time-series foundation models can address this issue, or whether they exhibit the same limitations as classical models. This study systematically benchmarks four zero-shot foundation models—Chronos-2 (Amazon), Google TimesFM 2.5, Salesforce MOIRAI, and Tsinghua Sundial—against an auto-tuned ARIMA baseline using epidemiological data from San Juan (Puerto Rico) and Iquitos (Peru) with climate covariates (temperature, dew point, precipitation, and vegetation indices). Evaluated over a 12-week forecasting horizon using exact metrics from verified experimental runs, model performance diverged across cities: Chronos-2 Multivariate achieved top accuracy in San Juan (RMSE 3.7604, MAE 3.1203), outperforming ARIMA (RMSE 3.9082), closely followed by Google TimesFM 2.5 (RMSE 4.0705) and Salesforce MOIRAI (RMSE 4.6054, MAE 4.1959). In Iquitos, Tsinghua Sundial flow-matching attained the minimal error (RMSE 2.5039, MAE 2.2139), closely followed by Google TimesFM 2.5 (RMSE 2.5487, MAE 2.2428) and Salesforce MOIRAI (RMSE 2.8459, MAE 2.5089). Beyond providing a model ranking, this study articulates the architectural reasons behind these performance differences. The findings conclude that model selection must be guided by the target city’s epidemic amplitude, the availability of computational resources, and the requirement for calibrated uncertainty estimates.

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Index Terms

Computer Science
Information Sciences

Keywords

Forecasting Dengue Fever Chronos-2 Salesforce MOIRAI TimesFM Tsinghua Sundial time series forecasting foundation model forecasting using LLMs multivariate forecasting epidemiological forecasting outbreak monitoring San Juan Iquitos climate-driven disease forecasting

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