Performance Prediction of Commodity Prices Using Foreign Exchange Futures

In an experimental quantitative research design, data from the Futures Market for commodities and foreign exchange futures covering 1986-2011 were obtained and addressed. A General Regression Neural Network was overlaid on this data to deduce a time-series prediction model for wheat prices. Performance prediction error was only 4.42%.https://scholarworks.waldenu.edu/archivedposters/1031/thumbnail.jp

Forecasting Chlorine Residuals in a Water Distribution System Using a General Regression Neural Network

Abstract: In a water distribution system (WDS), chlorine disinfection is important in preventing the spread of waterborne diseases. By strictly controlling residual chlorine throughout the WDS, water quality managers can ensure the satisfaction and safety of their customers. However, due to the travel time of water between the chlorine dosing point and any strategic monitoring points, water treatment plant (WTP) operators often receive information too late for their responses to be effective. Given the ability to forecast the chlorine residual at strategic points in a WDS, it would be possible to have superior control over the chlorine dose, thereby preventing incidents of under-and over-chlorination. In this research, a general regression neural network (GRNN) has been developed for forecasting chlorine residuals in the Myponga WDS to the south of Adelaide, South Australia, 24 hours in advance. A number of critical model issues are addressed including: selection of an appropriate forecasting horizon; division of the available data into subsets for modelling; and, the determination of the inputs that are relevant to the chlorine forecasts. In order to determine if the GRNN is able to capture any nonlinear relationships that may be present in the data set, a comparison is made between the GRNN model and a multiple linear regression (MLR) model. When tested on an independent validation set of data, the GRNN models were able to forecast chlorine levels to a high level of accuracy, up to 24 hours in advance. The GRNN also significantly outperformed the MLR model, thereby providing evidence for the existence of nonlinear relationships in the data set

Pollutant concentrations and Meteorological data classification by Neural Networks

This paper present an environmental contingency forecasting tool based on Neural Networks (NN). Forecasting tool analyzes every hour and daily Sulphur Dioxide (SO2) concentrations and Meteorological data time series. Pollutant concentrations and meteorological variables are self-organized applying a Self-organizing Map (SOM) NN in different classes. Classes are used in training phase of a General Regression Neural Network (GRNN) classifier to provide an air quality forecast. In this case a time series set obtained from Environmental Monitoring Network (EMN) of the city of Salamanca, Guanajuato, México is used. Results verify the potential of this method versus other statistical classification methods and also variables correlation is solved

Multi-score Learning for Affect Recognition: the Case of Body Postures

An important challenge in building automatic affective state recognition systems is establishing the ground truth. When the groundtruth is not available, observers are often used to label training and testing sets. Unfortunately, inter-rater reliability between observers tends to vary from fair to moderate when dealing with naturalistic expressions. Nevertheless, the most common approach used is to label each expression with the most frequent label assigned by the observers to that expression. In this paper, we propose a general pattern recognition framework that takes into account the variability between observers for automatic affect recognition. This leads to what we term a multi-score learning problem in which a single expression is associated with multiple values representing the scores of each available emotion label. We also propose several performance measurements and pattern recognition methods for this framework, and report the experimental results obtained when testing and comparing these methods on two affective posture datasets

Neural network-aided optimisation of a radio-frequency atomic magnetometer

Efficient unsupervised optimisation of atomic magnetometers is a requirement in many applications, where direct intervention of an operator is not feasible. The efficient extraction of the optimal operating conditions from a small sample of experimental data requires a robust automated regression of the available data. Here we address this issue and propose the use of general regression neural networks as a tool for the optimisation of atomic magnetometers which does not require human supervision and is efficient, as it is ideally suited to operating with a small sample of data as input. As a case study, we specifically demonstrate the optimisation of an unshielded radio-frequency atomic magnetometer by using a general regression neural network which establishes a mapping between three input variables, the cell temperature, the pump beam power and the probe beam power, and one output variable, the AC sensitivity. The optimisation results into an AC sensitivity of 44 fT/Hz at 26 kHz

The Impact of COVID-19 Shocks on Business and GDP of Global Economy

This study examines the relationship between COVID-19 shocks and GDP loss of different countries worldwide based on the seven scenarios of the epidemiological DSGE/CGE model of [McKibbin, W., & Fernando, R. (2020). The Global Macroeconomic Impacts of COVID-19: Seven Scenarios. Asian Economic Papers, 20(2): 1-30, MIT Press]. We implemented a panel data approach for 24 cross-sectional units with three periods and a general regression neural network. The economic and financial shocks consist of labor supply, equity risk premium, consumption demand, and government expenditure. The findings show that the consumption demand and equity risk premium shocks on GDP are more influential than the other shocks. Moreover, the results reveal that the most significant GDP loss is associated with Japan, Germany, and the US, respectively, which are industrialized countries with the most prominent automobile manufacturers. The lowest GDP loss is linked to Saudi Arabia, one of the world\u27s biggest oil producer countries