Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4375479 | Ecological Informatics | 2006 | 10 Pages |
Abstract
Ecological data can be difficult to collect, and as a result, some important temporal ecological datasets contain irregularly sampled data. Since many temporal modelling techniques require regularly spaced data, one common approach is to linearly interpolate the data, and build a model from the interpolated data. However, this process introduces an unquantified risk that the data is over-fitted to the interpolated (and hence more typical) instances. Using one such irregularly-sampled dataset, the Lake Kasumigaura algal dataset, we compare models built on the original sample data, and on the interpolated data, to evaluate the risk of mis-fitting based on the interpolated data.
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Authors
R.I. (Bob) McKay, Hoang Tuan Hao, Naoki Mori, Nguyen Xuan Hoai, Daryl Essam,