Air Quality Data-base Implementation By Using Time Series
Statistic Filling
Author(s)
G. Latini, G. Passerini & S. Tascini
Abstract
Air quality data-base implementation by using
time series statistic filling
G. Latini, G. Passerini & S. Tascini
Dipartimento di Energetica,
Ancona University, Italy
Abstract
In this work we present a set of procedures to achieve an extended air quality Data
Base starting from sets of raw data which can be affected by wide gaps due to
malfunction of sensors and/or weaknesses in collection software. The focus is on
the statistic filling of incomplete time series, after evaluating a short range of
methods.
In the first step we analyse statistics and substituted undoubtedly abnormal
isolated values by means of specific algorithms such as Wilcoxon rank sum test,
Box-Jenkins model, trend analysis, regression etc. [3]. Then we proceed with
missing data filling along time series choosing among linear interpolation, nearest
neighbor and spatial averaging algorithms. Standard filling protocols such as those
proposed by EPA (Environmental Protection Agency) and by NWS (National
Weather S
Keywords
Related Book
Other papers in this volume
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