Time series forecasting, such as stock price prediction, is one of the most important complications in the financial area as data is unsteady and has noisy variables, which are affected by many factors. This study applies a hybrid method of Genetic Algorithm (GA) and Artificial Neural Network (ANN) technique to develop a method for predicting stock price and time series. In the GA method, the output values are further fed to a developed ANN algorithm to fix errors on exact point. The analysis suggests that the GA and ANN can increase the accuracy in fewer iterations. The analysis is conducted on the 200-day main index, as well as on five companies listed on the NASDAQ. By applying the proposed method to the Apple stocks dataset, based on a hybrid model of GA and Back Propagation (BP) algorithms, the proposed method reaches to 99.99% improvement in SSE and 90.66% in time improvement, in comparison to traditional methods. These results show the performances and the speed and the accuracy of the proposed approach.
Recently, there has been a significant emphasis in the forecasting of the electricity demand due to the increase in the power consumption. Energy demand forecasting is a very important task in the electric power distribution system to enable appropriate planning for future power generation. Quantitative and qualitative methods have been utilizedpreviously for the electricity demand forecasting. Due to the limitations inthe availability of data, these methods fail to provide effective results. With the development of the advanced tools, these methods are replaced by efficient forecasting techniques. This paper presents the computational modeling of electricity consumption based on the Neural Network (NN) training algorithms. The main aim of the work is to determine the optimal training algorithm for electricity demand forecasting. From the experimental analysis, it is concluded that the Bayesian regularization training algorithm exhibits low relative error and high correlation coefficient than other training algorithms. Thus, the Bayesian Regularization training algorithm is selected as the optimal training algorithm for the effective prediction of the electricity demand. Finally, the economic input attributes are forecasted for next 15 years using time series forecasting. Using this forecasted economic attributes and with the optimal Bayesian Regularization training algorithm, the electricity demand for the next 15 years ispredicted. The comparative analysis of the NN training algorithms for the proposed dataset and larger datasets obtained from the UCI repository and American Statistical Association shows that the Bayesian Regularization training algorithm yields higher correlation value and lower relative error than other training algorithms.
This paper discusses the application of Neural Logic Networks in time series forecasting. Neural Logic Networks are systems that are developed to incorporate the strengths of neural networks and expert systems, which is equivalent to the human processes of logic and intuition [1]. This paper examines their prospect in forecasting of time series and compares their performance with linear models and the Feed Forward Neural Network. Additionally, the suitability of logic rules, generated from a Neural Logic Network, as potential inputs to forecasting systems is also examined. They are applied on two different meteorological series with strong features: a mean hourly wind speed series that exhibits behavior similar to random walk and an hourly solar radiation series selected because of its seasonal nátuře with discontinuities.
Several algorithms have been developed for time series forecasting. In this paper, we develop a type of algorithm that makes use of the numerical methods for optimizing on objective function that is the Kullbak-Leibler divergence between the joint probability density function of a time series xi, X2, Xn and the product of their marginal distributions. The Grani-charlier expansion is ušed for estimating these distributions.
Using the weights that have been obtained by the neural network, and adding to them the Kullback-Leibler divergence of these weights, we obtain new weights that are ušed for forecasting the new value of Xn+k.