A bootstrap test for additive outliers in non‐stationary time series

Astill, Sam; Harvey, David I.; Taylor, A. M. Robert

doi:10.1111/jtsa.12033

Cited by 4 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To that end, it is well known that simulation can be of particular importance in such a search. For example, there are multiple recent papers that rely on simulations to identify outliers while looking at the ordered properties of the random sample (Basu and Meckesheimer, 2007;Harvey et al, 2013), for a summary see (Gupta et al, 2013), however when the time series data is irregular such tests may give erroneous results. Another common methodology is to refer to the asymptotic distributions of such outliers (see for example, Fox, 1972;Tsay, 1988;Chen and Liu, 1993;Chen and Tiao, 1990), and thus based on these distributions and an index value, a particular observation is identified as one type of outlier or another.…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning and Heuristic Algorithms for Outlier Detection in Irregular Spatiotemporal Datasets

Chowdhury¹

2018

J ENV INFORM

View full text Add to dashboard Cite

A central problem in time series analysis is the detection of outliers, with further complications presented by irregular time series data measured having spatiotemporal components. This paper presents one Heuristic and two Supervised Machine Learning algorithms for the detection of outliers in this context in univariate time series data, with comparison of results to Chen and Liu's (1993) automatic outlier detection methodology. Due to the recent trend of set up of large environmental databases across many states in the US and around the world, which allow submission of pollutant measurement data from virtually any source, these procedures are applied to the measurements of various surface water pollutants in the California Environmental Data Exchange Network (CEDEN) for understanding and exploring the viability of such databases and the proposed methods. The proposed methodologies though not as robust, give similar results to existing methodologies given the nature of the data, but can be far less time intensive to implement providing interesting insights into the database. Thus, the algorithms presented can be widely used with minimal computing resource requirements with very tractable results even with very large datasets. The methodologies have wide applicability in a variety of contexts and a wide variety of databases with similar measurement challenges across many disciplines, specifically in the environmental setting. In particular, the results have large potential regulatory impact on accepted levels of different pollutants in California water bodies, as well as the amounts to be charged for industrial discharge into those water bodies, and is intended to provide direction for further research and regulatory investments. Based on the results it seems reasonable to assume that there is further room for the inclusion of nongovernmental agency pollutant measurements in the debate of environmental pollution, specifically in California. However, the results also indicate that the use of such databases in a more inclusive way for regulatory matters must be carefully evaluated on an individualized basis. That is to ensure that poorly collected/handled measurements, do not inundate the database over and above those collected with more rigor, thus potentially making inference on the true population distribution of the pollutants more difficult; being especially relevant for those pollutant measurements, which require more delicate sampling procedures.

show abstract