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

Modelling and prediction of surface roughness in cnc turning process using neural networks

© 2020, Strojarski Facultet. All rights reserved. The paper presents an approach to solving the problem of modelling and prediction of surface roughness in CNC turning process. In order to solve this problem an experiment was designed. Samples for experimental part of investigation were of dimensions Φ30 × 350 mm, and the sample material was GJS 500-7. Six cutting inserts were used for the designed experiment as well as variations of cutting speed, feed and depth of cut on CNC lathe DMG Moriseiki-CTX 310 Ecoline. After the conducted experiment, surface roughness of each sample was measured and a data set of 750 instances was formed. For data analysis, the Back-Propagation Neural Network (BPNN) algorithm was used. In modelling different BPNN architectures with characteristic features the results of RMS (Root Mean Square) error were controlled. Specially analysed were the RMS errors realised by different number of neurons in hidden layers. For the BPNN architecture with one hidden layer the architecture (4 – 8-1) was adopted with RMS error of 3,37%. In modelling the BPNN architecture with two hidden layers, a considerable amount of architectures was investigated. The adopted architecture with two hidden layers (4-2-10-1) generated the RMS error of 2,26%. The investigation was also directed at the size of the data set and controlling the level of RMS error