Forecasting annual inflation with seasonal monthly data: using levels versus logs of the underlying price index

This paper investigates whether using natural logarithms (logs) of price indices for forecasting inflation rates is preferable to employing the original series. Univariate forecasts for annual inflation rates for a number of European countries and the USA based on monthly seasonal consumer price indices are considered. Stochastic seasonality and deterministic seasonality models are used. In many cases, the forecasts based on the original variables result in substantially smaller root mean squared errors than models based on logs. In turn, if forecasts based on logs are superior, the gains are typically small. This outcome sheds doubt on the common practice in the academic literature to forecast inflation rates based on differences of logs.

Frying of potato chips in a blend of canola oil and palm olein: changes in levels of individual fatty acids and tocols

The changes occurring in the levels of nutritionally relevant oil components were assessed during repeated frying of potato chips in a blend of palm olein and canola oil (1:1 w/w). The blend suffered minimal reductions in omega-3 and omega-6 polyunsaturated fatty acids. There was no significant difference between the fatty acid composition of the oil extracted from the product and that of the frying medium, in all three cases. The blend also contained a significant amount of tocols which add a nutritional value to the oil. The concentration of the tocols was satisfactorily retained over the period of oil usage, in contrast to the significant loses observed in the case of the individual oils. The blend also performed well when assessed by changes in total polar compounds, free fatty acids, p-anisidine value. When fried in used oil, the product oil content increased progressively with oil usage time. This study shows that blended frying oils can combine good stability and nutritional quality

Automatic near real-time selection of flood water levels from high resolution Synthetic Aperture Radar images for assimilation into hydraulic models: a case study

Flood extents caused by fluvial floods in urban and rural areas may be predicted by hydraulic models. Assimilation may be used to correct the model state and improve the estimates of the model parameters or external forcing. One common observation assimilated is the water level at various points along the modelled reach. Distributed water levels may be estimated indirectly along the flood extents in Synthetic Aperture Radar (SAR) images by intersecting the extents with the floodplain topography. It is necessary to select a subset of levels for assimilation because adjacent levels along the flood extent will be strongly correlated. A method for selecting such a subset automatically and in near real-time is described, which would allow the SAR water levels to be used in a forecasting model. The method first selects candidate waterline points in flooded rural areas having low slope. The waterline levels and positions are corrected for the effects of double reflections between the water surface and emergent vegetation at the flood edge. Waterline points are also selected in flooded urban areas away from radar shadow and layover caused by buildings, with levels similar to those in adjacent rural areas. The resulting points are thinned to reduce spatial autocorrelation using a top-down clustering approach. The method was developed using a TerraSAR-X image from a particular case study involving urban and rural flooding. The waterline points extracted proved to be spatially uncorrelated, with levels reasonably similar to those determined manually from aerial photographs, and in good agreement with those of nearby gauges.