QUANTIFICATION FOR VISUALIZATION OF TIME TREND

A method for quantifying fluctuations in time-series data was develope d and tested to aid the process of visualization. The methodology is b ased on free-form sliding polynomials and identifies (a) short-period variability about the mean value, (b) a long-term trend or cycle, and (c) random errors residual to these two structured components. Consist ent results were obtained for designed synthetic data and natural data from seven sites in Georgia. Statistics of fit of the analytical mode l for the natural data were not significant on a site-by-site basis. A n unexpected finding for the study was obtained when the statistical r esults for the seven data sets for temperature were pooled. The smooth ing model yielded consistent long-term trends even though the individu al station results were not significant. Also, the correlation coeffic ients, while low, showed a statistically significant trend toward high er values toward the northwest and away from the Georgia coast line. T his study thus supports the concept that multiple-site, and regionally based, analyses are necessary for the detection of trends. Secondaril y, such consistency of results strengthens the conclusion that the pro posed smoothing method is an effective procedure in the presence of va rying amounts of random content in the natural data sets.