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In this paper, we propose a novel multi-variate algorithm using atriple-regression methodology to predict the airborne-pollen allergy seasonthat can be customized for each patient in the long term. To improve theprediction accuracy, we first perform a pre-processing to integrate thehistorical data of pollen concentration and various inferential signals fromother covariates such as the meteorological data. We then propose a novelalgorithm which encompasses three-stage regressions: in Stage 1, a regressionmodel to predict the start/end date of a airborne-pollen allergy season istrained from a feature matrix extracted from 12 time series of the covariateswith a rolling window; in Stage 2, a regression model to predict thecorresponding uncertainty is trained based on the feature matrix and theprediction result from Stage 1; in Stage 3, a weighted linear regression modelis built upon prediction results from Stage 1 and 2. It is observed and provedthat Stage 3 contributes to the improved forecasting accuracy and the reduceduncertainty of the multi-variate triple-regression algorithm. Based ondifferent allergy sensitivity level, the triggering concentration of the pollen- the definition of the allergy season can be customized individually. In ourbacktesting, a mean absolute error (MAE) of 4.7 days was achieved using thealgorithm. We conclude that this algorithm could be applicable in both genericand long-term forecasting problems.

A Multi-Variate Triple-Regression Forecasting Algorithm for Long-Term Customized Allergy Season Prediction