This paper presents a neural fuzzy network model for seasonal streamflow forecasting. The model is based on a constructive learning method where neurons groups compete when the network receives a new input, so that it learns the fuzzy rules and membership functions essential for modelling a fuzzy system. The model was applied to the problem of seasonal streamflow forecasting using a database of...

The popularity of realized measures and various linear models for volatility forecasting has been the focus of attention in the literature addressing energy markets’ price variability over the past decade. However, there are no studies to help practitioners achieve optimal forecasting accuracy by guiding them to a specific estimator and model. This paper contributes to this literature in two wa...

Electricity price forecasting is becoming more important in everyday business of power utilities. Good forecasting models can increase effectiveness of producers and buyers playing roles in electricity market. Price is also a very important element in investment planning process. This paper presents a forecasting technique to model day-ahead spot price using well known ARIMA model to analyze an...

Forecasting data from a time series is to make predictions for the future from available data. Thus, such a problem can be viewed as a traditional data mining problem because it is to extract rules for prediction from available data. There are two kinds of forecasting approaches. Most traditional forecasting approaches are based on all available data including the nearest data and far away data...

Effective reservoir operation under the effects of climate change is immensely challenging. The accuracy inflow forecasting one essential factors supporting operations. This study aimed to investigate coupling models feature selection (FS) and machine learning (ML) algorithms predict monthly inflow. was carried out using data from Huai Nam Sai in southern Thailand. Eighteen years recorded (i.e....

The purpose of this presentation is to evaluate and benchmark ensemble methods for time series prediction for daily currency exchange rates using ensemble methods based on feedforward neural networks and kernel partial least squares (K-PLS). Ensemble methods reduce the variance on the forecasts and allow for the assessment of confidence metrics and risk for the forecasting model. The use of neu...

A daily peak load forecasting technique that uses artificial neural network presented in this paper. A neural network of used to predict the daily peak load for a period available using one step ahead prediction load to the actual load. The ith index is used as load for the ith day of the year following networks are trained by the back propagation algorithm. from the Nigerian national electric ...

Abstract: Forecasting the future evolution of a system based only on past information comprises a central scientific problem. In this work we investigate the comparative performance of recurrent multi–layer perceptrons, trained through backpropagation through time and the differential evolution algorithm, to perform one–step–ahead predictions for the laser time series (Data set A) from the Sant...