Scheme depicting the original dataset of daily pollen concentrations for the period 2018–2022 and time series extracted for the simulation study.

<p>BZ2: Bolzano; VI1: Vicenza; NAs: missing data. Each season was obtained from the earlier start and later end day of the observed pollen seasons across the 2 monitoring stations: Season 2018 (start: 31/01/2018, <i>Alnus</i> BZ2; end: 17/09/2018, Poaceae VI1); Season 2019 (start:...

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Bibliographic Details
Main Author:	Sofia Tagliaferro (20291213) (author)
Other Authors:	Adrián Corrochano (20291216) (author), Pierpaolo Marchetti (3574394) (author), Alessandro Marcon (411991) (author), Soledad Le Clainche (20050254) (author)
Published:	2024
Subjects:	Genetics Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified two monitoring stations new method based moving mean interpolation demonstrating comparable performance 10 days ). novel imputation method imputation method used simulation settings ). moving mean algorithm aerobiology &# 8221 aerobiological data reconstruction assessed imputation accuracy imputation methods aerobiological datasets &# 8220 gsvd algorithm imputation error various proportions statistical approach specifically implemented simulation study results showed r package primarily influenced pollen variability pollen concentrations pollen characteristics physical patterns northeastern italy high variability gap lengths first time findings contribute driven approach
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Scheme depicting the original dataset of daily pollen concentrations for the period 2018–2022 and time series extracted for the simulation study.

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