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- W2251268965 abstract "A serious problem in analysing traffic count data is what to do when missing or extreme values (outliers) occur. Missing data occurs for a variety of reasons, for example the breakdown of automatic counters, and this absence makes the compilation of meaningful summary statistics difficult. Large outliers can also highlight periods of high and low demand which require special traffic management measures. Existing methods for dealing with missing values and outliers in transport data are crude, ranging from making estimates and detecting outliers by eye to a few computerised algorithms. There exists a necessity for such statistical techniques to be developed and applied in the transport field, in particular the need to be able to determine what are the true outliers in a time series. Little work, however, has been done to assess the relative merits of alternative methods." @default.
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- W2251268965 date "1992-01-01" @default.
- W2251268965 modified "2023-09-26" @default.
- W2251268965 title "A Comparative Study of Outlier Detection and Missing Value Estimation Methods Applied to Time Series Transport Data" @default.
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