Using intelligent optimization methods to improve the group method of data handling in time series prediction

In this paper we show how the performance of the basic algorithm of the Group Method of Data Handling (GMDH) can be improved using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The new improved GMDH is then used to predict currency exchange rates: the US Dollar to the Euros. The performance of the hybrid GMDHs are compared with that of the conventional GMDH. Two performance measures, the root mean squared error and the mean absolute percentage errors show that the hybrid GMDH algorithm gives more accurate predictions than the conventional GMDH algorithm

Métodos para la previsión de los precios del gas

The difficulty in gas price forecasting has attracted much attention of academic researchers and business practitioners. Various methods have been tried to solve the problem of forecasting gas prices however, all of the existing models of prediction cannot meet practical needs. In this paper, a novel hybrid intelligent framework is developed by applying a systematic integration of GMDH neural networks with GA and Rule-based Exert System (RES) employs for gas price forecasting. In this paper we use a new method for extract the rules and compare different methods for gas price forecasting. Our research reveals that during the recent financial crisis period by employing hybrid intelligent framework for gas price forecasting, we obtain better forecasting results compared to the GMDH neural networks and MLF neural networks and results will be so better when we employ hybrid intelligent system with for gas price volatility forecastingLa dificultad de la previsión de los precios del gas ha atraído considerablemente la atención de los investigadores universitarios y los profesionales del sector. A pesar de que se ha intentado solucionar el problema de la previsión de los precios del gas con diferentes métodos, ninguno de los modelos de predicción existentes llegan a cumplir con las necesidades prácticas. En este artículo, se ha desarrollado un novedoso sistema inteligente híbrido mediante la aplicación de la integración sistemática de redes neuronales de tipo Group Method of Data Handling (GMDH) con algoritmos genéticos (AG) y un sistema experto basado en reglas (SER) a la previsión de los precios del gas. Igualmente, utilizamos un nuevo método para extraer las reglas y comparar los diferentes métodos para la previsión de los precios del gas. Nuestra investigación revela que durante la reciente crisis económica se obtienen mejores resultados utilizando un sistema inteligente híbrido para la previsión de los precios del gas, en comparación con las redes neuronales de tipo GMDH y de tipo Multi-Layer Feed-forward (MLF), y que los resultados mejorarán si utilizamos un sistema inteligente híbrido en la previsión de la volatilidad de los precios del ga

Applying GMDH-Type Neural Network and Genetic Algorithm for Stock Price Prediction of Iranian Cement Sector

The cement industry is one of the most important and profitable industries in Iran and great content of financial resources are investing in this sector yearly. In this paper a GMDH-type neural network and genetic algorithm is developed for stock price prediction of cement sector. For stocks price prediction by GMDH type-neural network, we are using earnings per share (EPS), Prediction Earnings Per Share (PEPS), Dividend per share (DPS), Price-earnings ratio (P/E), Earnings-price ratio (E/P) as input data and stock price as output data. For this work, data of ten cement companies is gathering from Tehran stock exchange (TSE) in decennial range (1999-2008). GMDH type neural network is designed by 80% of the experimental data. For testing the appropriateness of the modeling, reminder of primary data were entered into the GMDH network. The results are very encouraging and congruent with the experimental result

Огляд гібридних структур МГУА-подібних нейронних мереж та генетичних алгоритмів

Стаття розкриває переваги поєднання МГУА-подібних нейронних мереж з генетичними алгоритмами та представляє найбільш вагомі результати щодо створення їх гібридних структур.Статья раскрывает преимущества сочетания МГУА-подобных нейронных сетей с генетическими алгоритмами и представляет наиболее значимые результаты по созданию их гибридных структур.This paper reveals benefits of the composition of GMDH-type neural networks and Genetic Algorithms and presents the most important results in creation of hybrid structures

Application of inclusive multiple model for the prediction of saffron water footprint

Applying new approaches in the management of water resources is a vital issue, especially in arid and semi-arid regions. The water footprint is a key index in water management. Therefore, it is necessary to predict its changes for future durations. The soft computing model is one of the most widely used models in predicting and estimating agroclimatic variables. The purpose of this study is to predict the green and blue water footprints of saffron product using the soft computing model. In order to select the most effective variables in prediction water footprints, the individual input was eliminated one by one and the effect of each on the residual mean square error (RMSE) was measured. In the first stage, the Group Method of Data Handling (GMDH) and evolutionary algorithms have been applied. In the next stage, the output of individual models was incorporated into the Inclusive Multiple Model (IMM) as the input variables in order to predict the blue and green water footprints of saffron product in three homogenous agroclimatic regions. Finally, the uncertainty of the model caused by the input and parameters was evaluated. The contributions of this research are introducing optimized GMDH and new ensemble models for predicting BWF, and GWF, uncertainty analysis and investigating effective inputs on the GWF and BWF. The results indicated that the most important variables affecting green and blue water footprints are plant transpiration, evapotranspiration, and yield, since removing these variables significantly increased the RMSE (range=11–25). Among the GMDH models, the best performance belonged to NMRA (Naked Mole Ranked Algorithm) due to the fast convergence and high accuracy of the outputs. In this regard, the IMM has a better performance (FSD=0.76, NSE=0.95, MAE) = 8, PBIAS= 8) than the alternatives due to applying the outputs of several individual models and the lowest uncertainty based on the parameters and inputs of the model (p = 0.98, r = 0.08)

Forecasting Stock Exchange Data using Group Method of Data Handling Neural Network Approach

The increasing uncertainty of the natural world has motivated computer scientists to seek out the best approach to technological problems. Nature-inspired problem-solving approaches include meta-heuristic methods that are focused on evolutionary computation and swarm intelligence. One of these problems significantly impacting information is forecasting exchange index, which is a serious concern with the growth and decline of stock as there are many reports on loss of financial resources or profitability. When the exchange includes an extensive set of diverse stock, particular concepts and mechanisms for physical security, network security, encryption, and permissions should guarantee and predict its future needs. This study aimed to show it is efficient to use the group method of data handling (GMDH)-type neural networks and their application for the classification of numerical results. Such modeling serves to display the precision of GMDH-type neural networks. Following the US withdrawal from the Joint Comprehensive Plan of Action in April 2018, the behavior of the stock exchange data stream and commend algorithms has not been able to predict correctly and fit in the network satisfactorily. This paper demonstrated that Group Method Data Handling is most likely to improve inductive self-organizing approaches for addressing realistic severe problems such as the Iranian financial market crisis. A new trajectory would be used to verify the consistency of the obtained equations hence the models' validity

Applying GMDH-type Neural Network and Particle warm Optimization for Prediction of Liquefaction Induced Lateral Displacements

Lateral spreading and flow failure are amongst the most destructive effects of liquefaction. Estimation of the peril of lateral spreading requires characterization of subsurface conditions, principally soil density, fine content, groundwater conditions, site topography and seismic characteristics. In this paper a GMDH-type neural network and particle swarm optimization is developed for prediction of liquefaction induced lateral displacements. Using this method, a new model was proposed that is suitable for predicting the liquefaction induced lateral displacements. The proposed model was tested before the requested calculation. The data set which is contains 250 data points of liquefaction-induced lateral ground spreading case histories from eighteen different earthquakes was divided into two parts: 70% were used as training and 30% were used as a test set, which were randomly extracted from the database. After initially testing on the input_output process, the predicted values were compared with experimental values to evaluate the performance of the group method of data handling neural network method

A data-driven approach using deep learning time series prediction for forecasting power system variables

This study investigates the performance of ‘Group Method of Data Handling’ type neural network algorithm in short-term time series prediction of the renewable energy and grid-balancing variables, such as the Net Regulation Volume (NRV) and System Imbalance (SI). The proposed method is compared with a Multi-layer Perceptron (MLP) neural network which is known as a universal approximator. Empirical validation results show that the GMDH performance is more accurate in compression with the most recent forecast which is provided by ELIA (Belgian transmission system operator). This study aims to practice the applicability of the polynomial GMDH-type neural network algorithm in time series prediction under a wide range of complexity and uncertainty related to the environment and electricity market

METACOGNITION REVEALED COMPUTATIONALLY

This paper focuses on current progress for the understanding of human cognition. Here different models have been considered such as MLP, FLANN, PNN, MLR, and HSN for recognition of one of the state of mind. It is argued that in addition to other models, PSO occupies a prominent place in the future of cognitive science, and that cognitive scientists should play an active role in the process. Baysian Approach in the same context has also discussed. The special case of predicting harm doing in a particular mental state has been experimented taking different models into account in depicting decision making as a process of probabilistic, knowledge-driven inference