Watson, S.M., Tight, M., Clark, S. and Redfern, E. (1991) Detection of Outliers in Time Series. Working Paper. Institute of Transport Studies, University of Leeds , Leeds, UK.Full text available as:
Available under licence : See the attached licence file.
As part of a SERC funded project this study aims to summarise the most relevant and recent literature with respect to outlier detection for time series and missing value estimation in traffic count data. Many types of transport data are collected over time and are potentlally suited to the application of time series analysis techniques. including accident data, ticket sales and traffic counts. Missing data or outliers in traffic counts can cause problems when analysing the data, for example in order to produce forecasts. At present it seems that little work has been undertaken to assess the merits of alternative methods to treat such data or develop a more analytic approach. Here we intend to review current practices in the transport field and summarise more general time series techniques for handling outlying or missing data.
The literature study forms the fist stage of a research project aiming to establish the applicability of time series and other techniques in estimating missing values and outlier detection/replacement in a variety of transport data. Missing data and outliers can occur for a variety of reasons, for example the breakdown of automatic counters. Initial enquiries suggest that methods for patching such data can be crude. Local authorities are to be approached individually usinga short questionnaire enquiry form in order to attempt to ascertain their current practices. Having reviewed current practices the project aims to transfer recently developed methods for dealing with outliers in general time series into a transport context. It is anticipated that comparisons between possible methods could highlight an alternative and more analytical approach to current practices. A description of the main methods ior detecting outliers in time series is given within the first section. In the second section practical applications of Box-Jenkins methods within a transport context are given. current practices for dealing with outlying and missing data within transport are discussed in section three. Recommendations for methods to be used in our current research are followed by the appendices containing most of the mathematical detail.
|Item Type:||Monograph (Working Paper)|
|Copyright, Publisher and Additional Information:||Copyright of the Institute of Transport Studies, University Of Leeds|
|Institution:||The University of Leeds|
|Academic Units:||The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds)|
|Depositing User:||Adrian May|
|Date Deposited:||18 May 2007|
|Last Modified:||04 May 2015 15:08|
|Publisher:||Institute of Transport Studies, University of Leeds|
|Identification Number:||Working Paper 362|