A Preliminary Evaluation of Price Forecasting Performance by Agricultural Economists

1981 ◽  
Vol 63 (4) ◽  
pp. 712-714 ◽  
Author(s):  
James C. Cornelius ◽  
John E. Ikerd ◽  
A. Gene Nelson
Keyword(s):  
Price Forecasting ◽  
Preliminary Evaluation ◽  
Forecasting Performance

scholarly journals ENHANCING THE ACCURACY OF MALAYSIAN HOUSE PRICE FORECASTING: A COMPARATIVE ANALYSIS ON THE FORECASTING PERFORMANCE BETWEEN THE HEDONIC PRICE MODEL AND ARTIFICIAL NEURAL NETWORK MODEL

2021 ◽  
Vol 19 (17) ◽  
Author(s):  
Nurul Fazira Sa’at ◽  
Nurul Hana Adi Maimun ◽  
Nurul Hazrina Idris
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural Network Model ◽  
House Price ◽  
Predictive Performance ◽  
Hedonic Price Model ◽  
Price Forecasting ◽  
Hedonic Price ◽  
Price Model ◽  
Forecasting Performance

The Hedonic Price Model (HPM), a prominent model used in real estate appraisal and economics, has been argued to be marred with nonlinearity, multicollinearity and heteroscedasticity problems that affect the accuracy of price predictions. An alternative method called Artificial Neural Network Model (ANN) was identified as capable of addressing the shortcomings of HPM and produces superior predictive performance. Hence, this study aims to evaluate the forecasting performance between HPM and ANN using Malaysian housing transaction data from the period between 2009 to 2018, sourced from the Valuation and Property Service Department, Johor Bahru. The models’ performance was evaluated and compared based on their statistical and predictive performance. Results showed that ANN outperformed HPM in both statistical and predictive performance. This study benefits the expansion of academic and practical knowledge in enhancing the accuracy of house price forecasting.

Grain Price Forecasting Using a Hybrid Stochastic Method

2017 ◽  
Vol 34 (05) ◽  
pp. 1750020 ◽  
Author(s):  
Yu Zhao ◽  
Xi Zhang ◽  
Zhongshun Shi ◽  
Lei He
Keyword(s):  
Genetic Algorithm ◽  
Random Search ◽  
Stochastic Method ◽  
Price Forecasting ◽  
Market Participants ◽  
Forecasting Models ◽  
Price Series ◽  
Forecasting Performance ◽  
Stochastic Time Series ◽  
Stochastic Time

Grain price forecasting is significant for all market participants in managing risks and planning operations. However, precise forecasting of price series is difficult because of the inherent stochastic behavior of grain price. In this paper, a novel hybrid stochastic method for grain price forecasting is introduced. The proposed method combines decomposed stochastic time series processes with artificial neural networks. The initial parameters of the hybrid stochastic model are optimized by a random search using a genetic algorithm. The proposed method is finally validated in China’s national grain market and compared with several recent price forecasting models. Results indicate that the proposed hybrid stochastic method provides a satisfactory forecasting performance in grain price series.

Preliminary Evaluation of a Weibull Function for Fitting Slow-Component Eye Velocity over the Time Course of Caloric-Induced Nystagmus

1989 ◽  
Vol 32 (3) ◽  
pp. 681-687 ◽  
Author(s):  
C. Formby ◽  
B. Albritton ◽  
I. M. Rivera
Keyword(s):  
Time Course ◽  
Slow Component ◽  
Weibull Function ◽  
Preliminary Evaluation ◽  
Mathematical Function ◽  
Before And After ◽  
Best Fitting ◽  
Eye Velocity ◽  
Fitting Parameters ◽  
Weibull Equation

We describe preliminary attempts to fit a mathematical function to the slow-component eye velocity (SCV) over the time course of caloric-induced nystagmus. Initially, we consider a Weibull equation with three parameters. These parameters are estimated by a least-squares procedure to fit digitized SCV data. We present examples of SCV data and fitted curves to show how adjustments in the parameters of the model affect the fitted curve. The best fitting parameters are presented for curves fit to 120 warm caloric responses. The fitting parameters and the efficacy of the fitted curves are compared before and after the SCV data were smoothed to reduce response variability. We also consider a more flexible four-parameter Weibull equation that, for 98% of the smoothed caloric responses, yields fits that describe the data more precisely than a line through the mean. Finally, we consider advantages and problems in fitting the Weibull function to caloric data.

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