This paper intends to propose an integrated method which combines selforganizing map (SOM) network with genetic algorithm (GA) and particle swarm optimization (PSO)-based (ISGP) algorithm to train the radial basis function (RBF) network for function approximation. The experimental results for three benchmark problems indicated that such integration can have better performance. In addition, usin...

abstract autoregressive integrated moving average (arima) has been one of the widely used linear models in time series forecasting during the past three decades. recent studies revealed the superiority of artificial neural network (ann) over traditional linear models in forecasting. but neither arima nor anns can be adequate in modeling and forecasting time series since the first model cannot d...

Predicting stock price is a trend yet very challenging task. It because the prices depend upon several internal and external factors. Stock prediction can be useful for financial sectors government help in informed decision-making. This paper analyzes market of K-Electric Karachi. found that K-electric on refinery sector. The data two sectors. Also, compares based moving average, auto-regressiv...

BACKGROUND Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic....

HIV-1 genome is highly heterogeneous. Due to this variation, features of HIV-I genome is in a wide range. For this reason, the ability to infection of the virus changes depending on different chemokine receptors. From this point of view, R5 HIV viruses use CCR5 coreceptor while X4 viruses use CXCR5 and R5X4 viruses can utilize both coreceptors. Recently, in Bioinformatics, R5X4 viruses have bee...

We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of an assigned output. In the first example, we use the daily weather dataset of the municipality of Naples (Italy) starti...

Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedur...

The popularity of lotteries has increased worldwide in the last few years, and so its revenues, making them an useful study case. This paper shows the research done for testing the randomness of the results of three lotteries and to check if there were any patterns, studying them as time series. This research was done using three methods, statistical randomness tests, autoregressive integrated ...

This paper applies the Hodrck-Prescott (HP) filter to forecast short-term residential real estate prices under cyclical movements. We separate the trend component from the cyclical component. We show that each regional residential market reacts not only to previous price movements, but also that these regional markets react to previous shocks under Auto Regressive Integrated Moving Average (ARI...