Deep learning is an exciting topic. It has been utilized in many areas owing to its strong potential. For example, it widely used the financial area which vital society, such as high-frequency trading, portfolio optimization, fraud detection and risk management. Stock market prediction one of most popular valuable finance. In this paper, proposes a stock model using Generative Adversarial Netwo...

this paper presents a fuzzy approach to the prediction of highly nonlinear timeseries.the optimized mamdani-type fuzzy system denoted sqp-flc is applied forthe input-output modeling of measured data. in order to tune fuzzy membershipfunctions, a sequential quadratic programming (sqp) method is employed. theproposed method is evaluated and validated on a highly complex time series, dailygold pri...

Public charging station occupancy prediction plays key importance in developing a smart strategy to reduce electric vehicle (EV) operator and user inconvenience. However, existing studies are mainly based on conventional econometric or time series methodologies with limited accuracy. We propose new mixed long short-term memory neural network incorporating both historical state sequences time-re...

With the rapid spread of urbanization, competent authorities become increasingly anxious from air pollution risks and effect on citizens especially those with respiratory diseases. In this work, performances six machine learning methods were analyzed for prediction maximum ozone (O_3) concentration next-day. The models make using concentrations atmospheric components (PM2.5, PM10, Ozone (O3), S...

Prior studies have claimed that nonhuman primates plan their routes multiple steps in advance. However, a recent reexamination of multi-step route planning in nonhuman primates indicated that there is no evidence for planning more than one step ahead. We tested multi-step route planning in capuchin monkeys using a pointing device to "travel" to distal targets while stationary. This device enabl...

This paper focuses on modeling and predicting the Internet end-to-end (e2e) delay multi-step ahead using Recurrent Neural Networks (RNNs). In this work, RoundTrip Time (RTT) is used as the basic metric to forecast the Internet e2e delay. A method for delay prediction model is developed using RNNs, able to model nonlinear systems. By observing the delay between two Internet nodes, RTT data has b...

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 ...

This research investigates the use of Ridge Polynomial Neural Network (RPNN) as non-linear prediction model to forecast the future trends of financial time series. The network was used for the prediction of one step ahead and five steps ahead of two exchange rate signals; the British Pound to Euro and the Japanese Yen to British Pound. In order to deal with a dynamic behavior which exists in ti...

Multi-step temporal-difference (TD) learning, where the update targets contain information from multiple time steps ahead, is one of the most popular forms of TD learning for linear function approximation. The reason is that multi-step methods often yield substantially better performance than their single-step counter-parts, due to a lower bias of the update targets. For non-linear function app...

This paper introduces a new structure in neural networks called TD-CMAC, an extension to the conventional Cerebellar Model Arithmetic Computer (CMAC), having reasonable ability in time series prediction. TD-CMAC, the conventional CMAC and a classical neural network model called Multi-Layer Perceptron (MLP) are simulated and evaluated for 1-hour-ahead prediction and 24-hour-ahead prediction of c...