Considering the fact that markets are generally influenced by different external factors, the stock market prediction is one of the most difficult tasks of time series analysis. The research reported in this paper aims to investigate the potential of **artificial** **neural** networks (ANN) in solving the forecast task in the most general case, when the time series are non-stationary. We used a feed-forward **neural** architecture: the nonlinear autoregressive **network** with exogenous inputs. The **network** training function used to update the weight and bias parameters corresponds to gradient descent with adaptive learning rate variant of the backpropagation algorithm. The results obtained using this technique are compared with the ones resulted from some ARIMA models. We used the mean square error (MSE) measure to evaluate the performances of these two models. The comparative analysis leads to the conclusion that the proposed model can be successfully applied to forecast the financial **data**. Keywords: **Neural** **Network**, Nonlinear Autoregressive **Network**, Exogenous Inputs, Time Series, ARIMA Model

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Recently, Reference [20] used the non-linear threshold method for investigating the variability and **forecasting** capability of South Australian seasonal rainfall with the aid of climate predictors where maximum of two climate drivers were used instead of single and found some successes regarding correlations and rainfall variability. Reference [20] suggested that the seasonal rainfall variability skill is largely depends on the influences and association of the multiple combination (more than two at a time) of climate drivers. However, there was a basic drawback of threshold method used in their study; it was not able to explore the complex rainfall-climate predictor’s relationships with the combinations of more than two climate drivers due to the limitations of this method. Because, the threshold method applied in the study was unable to derive mathematical model involving more than two climate drivers at a time. Reference [20] also suggested that a new statistical non- linear multi-variate ANN approach which is free from such limitations of input variables should be used with the threshold method in using the number of combinations of input variables at a time. Some studies have already shown that the multivariate non-linear ANN technique is able to map the complex rainfall-climate driver’s relationships with higher predictability compared to the threshold method used in the study of [20]. Such **an** emerging non-linear **artificial** intelligence technique was very rarely used for rainfall **forecasting** **purposes** in Australia using the large scale climate drivers as potential predictors. Few studies found are limited to some particular regions of Australia including the Queensland [14] and Victoria [5, 12-13]. For Victorian rainfalls, ANN models were developed for the seasonal rainfall **forecasting** and the ANN outputs were found to be superior compared to other techniques [5, 12-13].

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according to the idea mentioned above and it is illustrated in following Fig. 2. **Data** refining is very important stage, the Sensing phase filter unnecessary **data** and decrease **data** quantity for extracting relevant information from the historical stock **data**. The Detection phase validates the outcome of sensing phase by utilizing knowledge database (Prado et al., 2010). The Defend phase has higher perception level that analyzes a solution synthetically and provides **an** exact decision. Future trend can be efficiently predicted in this way. The SENSEX index of current stock market has the rise and fall in price and volume may change the result of the detection. To overcome this problem, AIS system updates the knowledge database and the detectors, so the system will adjust itself to discover effectively the changes of external factors.

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Any ANN architecture has different features for training, such as, topology of the **network**, types of **data**, number of neurons in each layer, forms of activations, the weights and parameter settings of the **network**. There- fore, these criteria play important roles to construct the best **network**. In this study, amongst ANN functions, the back-propagation (BP) algorithm in feed-forward **network** with different topologies was assessed in order to obtain high quality patterns with the best **forecasting** capacity. However, the training process carries on updating and changing the connectivity weights up to the satisfactory level, the drawbacks of over-fitting and inefficient optimal topologies can reduce the accuracy of the **network**. Thus, ICA was employed in the training procedure of ANN to initialize the weights of the **network**. The variance of the predicted output and target output was consid- ered as **network** error. In fact, reducing the **network** error was the main purpose in ANN training. Consequently, the Mean Square Error (MSE) was considered as a cost function in ICA. Hence, the most important goal of pro- posed algorithm was to minimize MSE cost function. As a result, ICA was obtained by means of the subsequent factors: the number of initial counties set to 100; imperialists set to 15 and coefficient â set to 2.

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As previously indicated, the inal objective of any modelling is to determine the concentrations of a given pollutant for a known set of input **data**. By training with some known results **an** artiicial **neural** **network** model (ANN), you can perform this task much more easily. To understand the capabilities of ANN, we irst begin with a brief introduction. Later, we show a particular **forecasting** model whose use is relevant to the subject of air quality modelling. Artiicial **Neural** Networks represent **an** advanced machine learning computing approach extensively used in the ield of pattern recognition, dynamic system prediction, control, and optimisation (Zhang, Patuwo, 1998). The **network** is made by computational units (artiicial neurons) through which information processing occurs by passing signals (often binary or real values) through links connecting **network** nodes. Each connection link has **an** associated weight representing its strength, and each node typically applies a nonlinear transformation, called **an** activation function, to its input to determine its output value. A **neural**

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The function f is the unit's activation function. The simplest case is that f is the identity function, and the output of the unit is just its input. This is called a linear unit. In the cases that function f o p esses the et fu tio i hi h ith the a i g i et the output does ’t change significantly the activation function is usually a sigmoid function. **Neural** networks can be multiple layers, these layers called hidden layers. Layers can be cascaded to form multiple layers. In this case the output of each layer can be the input of the next layer, multiple layer networks have more capacity and ability to calculate in comparison with a single layer **network**. The **network** is learned in order to have a desired output with the specific group of **data**, these inputs and outputs can be considered as vectors. The **network** is trained through the sequential input vectors and adjusting the weights with a specific methods that have been known. In the training procedure the weights of **network** are going to be convergent gradually, and with the obtained weights; output **data** can be obtained by the input **data**. In this study the electricity price is forecasted through time series **forecasting** and **neural** **network** time series methods in Ontario electricity market.

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In this study, we have evaluated the Functional Link **Artificial** **Neural** **Network** (FLANN) model for the task of pattern classification in **data** mining. The FLANN model functionally expands the given set of inputs. These inputs are fed to the single layer feed forward **artificial** **neural** **network**. The **network** is trained like Back propagation training methods. The experimental studies demonstrated that the FLANN model performs the pattern classification task quite well. In most cases, the results obtained with the FLANN model proved to be as good as or better than the best results found by the other models and algorithms with which it has compared. The performance of the FLANN models is remarkable in terms of processing time, which is also treated as one of the crucial aspect in **data** mining community. For all the databases described in the experimental studies, the models converged in **an** order of magnitude of less than one min of processing time on a Pentium IV 500 MHz computer.

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Estimation of global solar radiation (GSR) is important in most solar energy applications, particularly in design methods, in system characterization and in decision making for energy management. In this paper, a new methodology based on **artificial** **neural** networks (ANN) for generating daily GSR **data** is presented. By modeling GSR in regions where historical records are available, solar potential map for other sites that GSR have not been recorded was generated. In order to examine the ANN models, meteorological **data** throughout the year 2008 belonging to Karaj city in Alborz province of Iran were used to develop GSR predictors. Input parameters were maximum temperature, relative sunshine duration and extraterrestrial solar radiation while the output parameter was the solar radiation. Various networks were designed and tested and the most accurate model was selected. The best **network** was found as one hidden layer **network** with 3-4-1 topology, i.e., a **network** having four neurons in its hidden layer. To estimate the differences between the measured and predicted values, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R 2 ) were computed as0.66, 0.52, 4.46% and 0.978, respectively. The optimum ANN model was then used to predict GSR in other cities in the province. **Data** from three stations located in Hashtgerd, Taleghan and Chitgar cities were used as production set. The GSR values for production sites of Hashtgerd, Taleghan and Chigrar were calculated as 4.93, 4.35 and 5.08 kWh m -2 day -1 , respectively. Finally, the predicted solar potential values in all stations were integrated and represented in the form of a map. While results are site-specific, the methodology introduced here is general and provides **an** inexpensive means for GSR prediction based on readily available **data**.

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The Genetic Algorithm was inspired by the natural process of evolution and was considered as **an** optimization method where it, as a guided search method, tries to find **an** acceptable or satisfactory solution in a search space. The problem being solved by a GA is viewed as a black box system with a number of input parameters and one output parameter. In a more formal way, the input parameters that describe a possible solution are encoded in a representation called the chromosome, whereas the output parameter is usually the result of a function that captures the fitness of the candidate solution to the problem being addressed. The goal is finding a combination of input parameters’ values resulting in a satisfactory value for the output parameter [42]. To find **an** optimal solution, GA starts with **an** initial population of the potential solutions for the underlying problem. Each solution in the population, termed **an** individual, is evaluated by the problem specific fitness function reflecting the problem goal. Afterwards, the initial population is evolved by applying genetic operators that mimic the natural process of evolution. The canonical GA considers, mating selection, parent recombination, mutation and replacement operators. This way the initial population is replaced by a new generation by mating the elitists of the initial population so that the individuals in the new generation are expected to be more fit to the problem. The evolution process continues by producing a number of generations, expecting that, eventually after **an** appropriate number of generations, suitable individuals in terms of their fitness are available. The following subsections describe the GA operators.

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Supported (C-SS) and Simply Supported-Simply Supported (SS-SS) is determined using ANN technique. Initially, the first ten natural frequency parameters of the beam are found adopting Bernoulli-Euler beam theory, and then natural frequencies are computed theoretically. With the aid of theoretically obtained results the **data** sets are formed and ANN models are constructed. Here, 36 models are developed using primary 3 models. The results are found from these models by changing the number and properties of the neurons and input **data**. The handiness of the present models is examined by comparing the results of these models with theoretically obtained results. The effects of the number of neurons, input **data** and training function on the models are investigated. In addition, multiple regression models are developed with the **data**, and adjusted R 2 is investigated for determining the inefficient input parameters. To the best of authors knowledge, although various studies are presented on the free vibration analysis of the structures using ANN technique, the effects of the number of neurons, input **data** and training function on the models are not investigated in detail, and the inefficient input parameters are not determined using multiple regression models. In the present work, **an** attempt is made for addressing these issues.

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Especially over highly reflecting surfaces like sea ice, the used cloud mask may have problems. To enhance **data** qual- ity for further studies, a **data** mask with a defined threshold or weighting of involved pixels can be applied after the grid- ding routine. In the 500 m grid, single pixels with a high melt pond signal occur often within cloudy fields or at the edges of the cloud mask. We assume, that these pixels are a mis- classified cloud signal. To minimize the influence of these misclassified pixels, the above introduced technique elimi- nates at least grid cells with a low **data** quality (see Sect. 3). However, the existing problem of cloudy pixel in the initial dataset can impact the melt pond fraction and should be con- sidered.

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To select the best model it can be regarded as one that has a lower MAPE, which corresponds to the template whose sequence provided overnights is closer to the actual nights, or can select the model with the highest (r) corresponding one whose sequence forecasts of overnights follows best variations of the behavior of actual overnight stays. Not always the model with the best MAPE (lowest) (r) has a better (higher). Thus, since the values are very close between 8 selected models presented in Table 2 and any one of them could be used for the purpose of prediction of overnights. However the model in 4 presents a fairly low MAPE value (6,50%), almost the lowest value, and simultaneously a high (r) (0,696) for the remaining models, at the test set. This model also features very good results when considered all **data** (training, validation and test sets) with a MAPE value of 1,13% and the (r) of 0,982.

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We compare our proposed algorithm (GRNN-Ensemble) with existing algorithms (ARIMA-GARCH methodology, MLP, GS (GRNN with a single predictor) and GM (GRNN with multiple predictors)) on thirty synthetic datasets and ten real-world datasets. The real-world datasets (obtained from http://statistik.mathematic.uniwuerzburg.de/timeseries) are the Airline **Data**, the Bankruptcy **Data**, the Electricity **Data**, the Hong Kong **Data**, the Nile **Data**, the Share **Data**, the Star **Data**, the Car **Data**, the Sunspot **Data**, and the Temperatures **Data**. The algorithms are applied to make 10-step-ahead out-of-sample forecasts. These algorithms were ranked in terms of their accuracy in the interval estimation, and interval length. We assign rank 1 to the best algorithm, the rank 2 to the next best algorithm and so on. Table 1 summarizes the results. Obviously, the higher the accuracy the better. The lower the average interval length, the better the performance of the algorithms and the lower the standard deviation, the more consistent and reliable the algorithm. Appendix Tables A1-A4 give **an** overview of the statistical test results.

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This paper discusses the results of a mixed grinding simulator application based on **an** **artificial** **neural** **network** (multiple-layer perceptron using a back-propagation- like algorithm with moment). The **data** used came from a previous paper entitled “Selective grinding of dolomite and quartz mixes”. The Shewhart control chart for individual values was used in order to verify the statistical stability of the simulation process results, which was useful for testing acceptance. The results have displayed good performance of this tool related to mix grinding simulation, a common issue in the mining and metallurgical sectors.

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In hot working process, the prediction of material constitutive relationship can improve the optimization design process. Recently, the **artificial** **neural** **network** models are considered as a powerful tool to describe the elevated temperature deformation behavior of materials. Based on the experimental **data** from the isothermal compressions of 42CrMo high strength steel, **an** **artificial** **neural** **network** (ANN) was trained with standard back-propagation learning algorithm to predict the elevated temperature deformation behavior of 42CrMo steel. The inputs of the ANN model are strain, strain rate and temperature, whereas flow stress is the output. According to the predicted and experimental results, it indicates that the developed ANN model shows a good capacity of modeling complex hot deformation behavior and can accurately tracks the experimental **data** in a wide temperature range and strain rate range. In addition, the predicted **data** outside of experimental conditions were obtained, indicating good prediction potentiality of the developed ANN model. The q - s curves outside of experimental conditions indicate that the predicted strain-stress curves exhibit a typical dynamic recrystallization softening characteristic of high temperature deformation behavior. Through the coupling of the ANN model and finite element model, the hot compression simulations at the temperature of 1273 K and strain rates of 0.01~10 s –1 were conducted. The results show that the predicted constitutive **data** outside

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In this context, this paper examines the accuracy of a **forecasting** model in predicting tourism demand, as represented by the number of Monthly Guest Nights in Hotels in the North of Portugal. The **Artificial** **Neural** Networks (ANN) methodology is employed to forecast the volume of tourist from January 1987 to December 2006. This methodology was inspired in the biologic theories of human brain function. The human brain is composed of several non-linear processors densely interconnected operating in parallel, these being the principal advantages compared with other forecast techniques. This was achieved through a study of the reference time series whose past values were known and whose objective was to obtain a model that better predicts the behaviour of the time series under study. This paper is organized in the following structure: first, there is **an** overview section that examines the summary theoretical foundation of **neural** networks. Based on the theoretical analysis, a **neural** **network** is developed for **forecasting** the “Monthly Guest Nights in Hotels” in Northern Portugal. Real **data** from official publications in Portugal is used for the **neural** **network** development. The analysis results of **forecasting** are described in the next section. Some concluding remarks are given in the final section.

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The number of inputs in the input layer depends on the number of inputs we use in **an** actual system which is to be realized by **an** ANN. There can be any number of hidden layer and each hidden layer may contain any number of neurons depending on the application we use. Each interconnection between neurons takes a value called weight. During the learning or reasoning mode of **an** ANN the weights tends to change. The weights are changed by implementing some learning algorithm. The general goal of the learning algorithm is to reduce error and to make the **neural** **network** to learn effectively using the inputs and the targets [12]. There are many types of learning algorithms and each of them is used for different purpose and for different **data** sets.

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The model has 4 neurons in the hidden layer with the logistic activation function and was trained using the Resilient Backpropagation algorithm (a variation of backpropagation algorithm). The ANN model has the 12 preceding values as the input. The analysis of the output forecast **data** of the selected ANN model showed a reasonably close result compared to the target **data**. In other words, the model produced, according to the criteria of MAPE a highly accurate forecast. Therefore it can be considered adequate for the purpose of predic- tion in the reference time series. But the best model in most real world **forecasting** situations should be the one that is robust and accurate for a long time horizon and thus users can have confidence to use the model fre- quently.

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Many power systems not only are being pushed to their limits to meet their customers’ demands, but also spend a lot of resources in their operation scheduling. System security is becoming **an** ever more important issue in modern electrical power systems. It is known for all that electrical energy cannot be stored efficiently, due to this fact that the electrical load could be controlled by utilities only to a very small extent, the **forecasting** of load demand **data** forms **an** important component in planning generation schedules in a power system.

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