Forecasting currency exchange rate time series with fireworks-algorithm-based higher order neural network with special attention to training data enrichment

Exchange rates are highly fluctuating by nature, thus difficult to forecast. Artificial neural networks (ANN) have proved to be better than statistical methods. Inadequate training data may lead the model to reach suboptimal solution resulting, poor accuracy as ANN-based forecasts are data driven. To enhance forecasting accuracy, we suggests a method of enriching training dataset through exploring and incorporating of virtual data points (VDPs) by an evolutionary method called as fireworks algorithm trained functional link artificial neural network (FWA-FLN). The model maintains the correlation between the current and past data, especially at the oscillation point on the time series. The exploring of a VDP and forecast of the succeeding term go consecutively by the FWA-FLN. Real exchange rate time series are used to train and validate the proposed model. The efficiency of the proposed technique is related to other models trained similarly and produces far better prediction accuracy

Agricultural Commodity Price Forecasting using PSO-RBF Neural Network for Farmers Exchange Rate Improvement in Indonesia

Agricultural commodity price forecasting becomes important for farmers since the knowledge of agriculture commodity price fluctuation can help the farmers to identify the right selling time. Recently, the absence of such the forecasting system makes the farmers decide to sell their commodities to middlemen which in turn, reduces their exchange rate as the length of distribution flow is complicated. The length of distribution flow is started from farmers, middlemen, wholesalers, retailers, and consumers. To address this problem, a forecasting system based on radial basis function neural network (RBFNN) is proposed. To optimize the network’s learning process, particle swarm optimization (PSO)-based learning technique is applied. The RBFNN is chosen because of its ability to generally track irregular signal changing, good speed in learning process and robustness. Meanwhile, the implementation of PSO aims to improve weight values towards global optimum in RBFNN model

Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression

Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlineary carbon prices. In this study, we propose an empirical mode decomposition-based evolutionary least squares support vector regression multiscale ensemble forecasting model for carbon price forecasting. Firstly, each carbon price is disassembled into several simple modes with high stability and high regularity via empirical mode decomposition. Secondly, particle swarm optimization-based evolutionary least squares support vector regression is used to forecast each mode. Thirdly, the forecasted values of all the modes are composed into the ones of the original carbon price. Finally, using four different-matured carbon futures prices under the European Union Emissions Trading Scheme as samples, the empirical results show that the proposed model is more robust than the other popular forecasting methods in terms of statistical measures and trading performances

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management