A new accuracy measure based on bounded relative error for time series forecasting

Many accuracy measures have been proposed in the past for time series forecasting comparisons. However, many of these measures suffer from one or more issues such as poor resistance to outliers and scale dependence. In this paper, while summarising commonly used accuracy measures, a special review is made on the symmetric mean absolute percentage error. Moreover, a new accuracy measure called the Unscaled Mean Bounded Relative Absolute Error (UMBRAE), which combines the best features of various alternative measures, is proposed to address the common issues of existing measures. A comparative evaluation on the proposed and related measures has been made with both synthetic and real-world data. The results indicate that the proposed measure, with user selectable benchmark, performs as well as or better than other measures on selected criteria. Though it has been commonly accepted that there is no single best accuracy measure, we suggest that UMBRAE could be a good choice to evaluate forecasting methods, especially for cases where measures based on geometric mean of relative errors, such as the geometric mean relative absolute error, are preferred

Performant summative 3D rendering of voxel-wise MRF segmentation data

Visualization of Magnetic Resonance Fingerprinting segmented data presents significant difficulty because of the abstraction from the usual appearance and contrast of MR images. We present a method of rendering any probability-based tissue fraction partial volume ROIs in three dimensions using additive voxelized volumetric rendering as a form of segmentation. Datasets consist of n groups of segmented maps with each voxel representing the probability of a given tissue converted into 3D textures usable by the GPU to perform raymarched additive rendering. This allows for different tissue classifications within the dataset to be faded in and out with minimal human involvement