Absolute Percent Error Based Fitness Functions for Evolving Forecast Models
نویسندگان
چکیده
One aspect of evolutionary computing as a method of data mining, is its intrinsic ability to drive model selection according to a mixed set of criteria. Based on natural selection, evolutionary computing utilizes evaluation of candidate solutions according to a ftness criteria that ~xfight or might not share the exact same implementation as the metric used to measure the performance of the selected solution. This paper presents the results of using four different fitness functions to evolve nai’ve Bayesian networks based on a combination of Mean Absolute Percent Error and Worst Absolute Percent Error values tbr individual population members. In addition to the error measurements tiom both the training and lbrecast evaluations, data is presented that shows APE lbr individual members during the tbrecast generation and evahtation phase.
منابع مشابه
Comparison of Neural Network Models, Vector Auto Regression (VAR), Bayesian Vector-Autoregressive (BVAR), Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) Process and Time Series in Forecasting Inflation in Iran
This paper has two aims. The first is forecasting inflation in Iran using Macroeconomic variables data in Iran (Inflation rate, liquidity, GDP, prices of imported goods and exchange rates) , and the second is comparing the performance of forecasting vector auto regression (VAR), Bayesian Vector-Autoregressive (BVAR), GARCH, time series and neural network models by which Iran's inflation is for...
متن کاملImproved Rheological Model of Oil-Based Drilling Fluid for South-western Iranian Oilfields
In this study, predictive capabilities of apparent viscosity of oil-based drilling fluids which is used in National Iranian South Oilfields Company (NISOC) were evaluated using Newtonian and non-Newtonian models to drive a new suitable equation. The non-Newtonian models include Bingham plastic, Power law, Herschel-Bulkley, Casson, and Robertson-Stiff. To validate the results, the calculated vis...
متن کاملBrent crude oil Price Forecast with Hybrid Model of Nonlinear Grey Model and Linear Arima Waste Correction
The characteristics of crude oil and the factors affecting the price of this energy carrier have caused its price forecast to always be considered by researchers, oil market activists, governments and policy makers. Since the price of crude oil is affected by many factors, therefore, continuous studies should be done in this way so that the estimates made over time, the results are more accurat...
متن کاملTesting the four models for prediction of gully head advancement (case study: Hableh Rood basin- Iran)
Gully erosion is one of the most complicated and destructive forms of water erosion. In order to prevent this erosion, the important factors advancing gully head must be recognized. Nowadays, several models have been proposed in measuring gully head advancement and identifying the severity of erosion. These models must be calibrated for each country to see whether they are applicable or not. So...
متن کاملSemiparametric ARX neural-network models with an application to forecasting inflation
We examine semiparametric nonlinear autoregressive models with exogenous variables (NLARX) via three classes of artificial neural networks: the first one uses smooth sigmoid activation functions; the second one uses radial basis activation functions; and the third one uses ridgelet activation functions. We provide root mean squared error convergence rates for these ANN estimators of the conditi...
متن کامل