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Forecasting the returns of stock markets is gaining importance nowadays in finance. For this aim, in the last decade, Artificial Neural Networks (ANN) have been widely used to forecast stock market movements. In Baltic countries, artificial neural networks are not commonly used in predicting financial failures. This study aims using artificial neural networks to predict OMX Baltic Benchmark GI (OMXBBGI)...
Financial market dynamics forecasting has long been a focus of economic research. A hybridizing functional link artificial neural network (FLANN) and improved particle warm optimization (PSO) based on wavelet mutation (WM), named as IWM-PSO-FLANN, for forecasting the CSI 300 index is proposed in this paper. In the training model, it expands a wider mutation range while apply wavelet theory to the...
The severity of global magnetic disturbances in Near-Earth space can crucially affect human life. These geomagnetic disturbances are often indicated by a Kp index, which is derived from magnetic field data from ground stations, and is known to be correlated with solar wind observations. Forecasting of Kp index is important for understanding the dynamic relationship between the magnetosphere and solar...
One decision in Stock Market can make huge impact on an investor's life. The stock market is a complex system and often covered in mystery, it is therefore, very difficult to analyze all the impacting factors before making a decision. In this research, we have tried to design a stock market prediction model which is based on different factors. The model was built to predict performance of KSE-100...
The forecast of Singapore condominium prices is important for potential buyers to make informed decisions. This paper applies two algorithms to predict Singapore housing market and to compares the predictive performance of artificial neural network (ANN) model, i.e., the multilayer perceptron, with autoregressive integrated moving average (ARIMA) model. The more superior model is used to predict the...
In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index, hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are...
Precise estimation of the ratio of carotenoid to chlorophyll (Car/Chl) in crop is very important in precision agriculture and early detection of stress. Car/Chl value in leaves is considered as an indicator of stress, especially in the middle-layer and bottom-layer of crop. Traditional remote estimation of Car/Chl was based on the data obtained from the nadir. However, there are few studies focusing...
Predictive queries on moving objects offer an important category of location-aware services based on the objects' expected future locations. A wide range of applications utilize this type of services, e.g., traffic management systems, location-based advertising, and ride sharing systems. This paper proposes a novel index structure, named Predictive tree (P-tree), for processing predictive queries...
This paper investigates the use of support vector machine (SVM) to forecast hourly solar irradiance for a tropical country. The hourly irradiance data was obtained from Sepang Malaysia, recorded throughout 2011. The data is converted into corresponding clearness index values to facilitate model convergence. The forecast is tested against the standard multilayer perceptron (MLP) technique and persistence...
In Turkey, similarly to other grain producing countries, the prediction of wheat yield is an important problem. The objective in this study is to build an artificial neural network model that could effectively predict wheat yield by using meteorological data such as temperature and rainfall records. Multi-Layer Perceptron neural network model was chosen and the performance of the built network was...
Modern technologies such as DNA microarray have been developed to study the transcriptome of cancer cells. It has been used in many studies for tumor classification and of identification of marker genes associated with cancer. However, this technique often suffers the ‘curse of dimensionality’. A general approach to overcome this setback is to perform feature selection technique prior to classification...
This paper proposes the development of a Neural Network (NN) model for the prediction of the F2 layer critical frequency (foF2) at the magnetic equator over Chumphon (10.72°N, 99.37°E, dip angle 3.3°N), Thailand and then compared with the IRI model and the experimental ones. The feed forward network with backpropagation algorithm has been developed for predicting the foF2 values. The NN is trained...
The present study is carried out to predict the area and Rice production of Upper Brahmaputra Valley Zone of Assam using Artificial Neural Network (ANN). Multilayer Perceptron (MLP) with single hidden layer has been trained with the secondary data of the area, Rice production and meteorological data. Area and Rice production data are collected from the Directorate of Economics and Statistics, Directorate...
Recently, terrorist or rebel activity is experienced in many parts of the world. The objectives of the research are to design and develop an accurate predicting the distribution range radius and elapsing time of a terrorist situation. An Analytical Network Process (ANP) is used to classify salient quantitative and qualitative factors of the unsettled area, or terrorist behaviour. Then, the resulting...
This research investigated a retail sales forecasting problem based on early sales. An effective multivariate intelligent decision-making (MID) model is developed to handle this problem by integrating a data preparation and preprocessing module, a harmony search-wrapper-based variable selection (HWVS) module and a multivariate intelligent forecaster (MIF) module. The HWVS module selects out the optimal...
This paper presents an intelligent model for stock market signal prediction using Multi Layer Perceptron (MLP) Artificial Neural Networks (ANN). Blind source separation technique, from signal processing, is integrated with the learning phase of the constructed baseline MLP ANN to overcome the problems of prediction accuracy and lack of generalization. Kullback Leibler Divergence (KLD) is used, as...
In order to avoid shortcomings of the standard BP network algorithm, the forecast model in index of shanghai stock is constructed based on genetic neural network. Involving the advantages of GA and BP, the algorithm can simultaneously complete genetic selection within a solution space to find the optimal points. Then the BP algorithm searchs the best optimal result from those points by the direction...
The main purpose of forecasting in financial markets is to estimate future trends and to reduce risks of decision making. This research suggests an ANFIS model to improve prediction accuracy in stock price forecasting. For doing so, we applied fuzzy subtractive clustering for structure identification of our ANFIS model. We implemented the proposed model for predicting Tehran Stock Exchange Price Index...
Based on the analysis of driving forces of urban land expansion by Principal component analysis (PCA), this paper established a predicting model of urban built-up area for future by using socio-economical data. Being good at the performance of nonlinear approximation, artificial neural network (ANN), especially the back propagation algorithm (BP), is applied in the prediction of bulit-up land and...
With the development of the Rough Sets and Neural Network, dynamic prediction research on financial crisis has become as a developing trend. Based on this situation, this paper makes use of the Rough Sets' attribute reduction technique to reduce the financial index firstly, then imposes the Neural Network to train network so as to establish financial crisis alarming model to drop out enterprise's...
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