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.
The paper deals with a description of a constructive neural network based on gradient initial setting of its weights. The network has been used as a pattern classifier of two dimensional patterns but it can be generally used to n x m associative problems. A network topology, processes of learning and retrieving, experiments and comparison to other neural networks are described.
The present work proposes a hybrid neural network based model for rainfall prediction in the Southern part of the state West Bengal of India. The hybrid model is a multistep method. Initially, the data is clustered into a reasonable number of clusters by applying fuzzy c-means algorithm, then for every cluster a separate Neural Network (NN) is trained with the data points of that cluster using well known metaheuristic Flower Pollination Algorithm (FPA). In addition, as a preprocessing phase a feature selection phase is included. Greedy forward selection algorithm is employed to find the most suitable set of features for predicting rainfall. To establish the ingenuity of the proposed hybrid prediction model (Hybrid Neural Network or HNN) has been compared with two well-known models namely multilayer perceptron feed-forward network (MLP-FFN) using different performance metrics. The data set for simulating the model is collected from Dumdum meteorological station (West Bengal, India), recorded with in the 1989 to 1995. The simulation results have revealed that the proposed model is significantly better than traditional methods in predicting rainfall.