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