Introduction: Air pollutants and respiratory diseases have a significant relationship and cause major health problems. Low attention has been paid to the daily hospital admissions due to the presence of pollutants in desert cities such as Yazd city, Iran. Therefore, this study aimed to investigate the short-term relationship between air pollution and daily hospital admissions due to respiratory...

When analyzing a financial asset, it is essential to study the trend of its time series. It also necessary examine evolution and activity over statistically analyze possible future behavior. Both retail institutional investors base their trading strategies on these analyses. One most used techniques series dynamic structure using auto-regressive models, simple moving average models (SMA), mixed...

Electroencephalographic (EEG) data is widely used as a biosignal for the identification of different mental states in the human brain. EEG signals can be captured by relatively inexpensive equipment and acquisition procedures are non-invasive and not overly complicated. On the negative side, EEG signals are characterized by low signal-to-noise ratio and non-stationary characteristics, which mak...

Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Fuzzy autoregressive integrated moving average (FARIMA) models are the fuzzy improved version of the autoregressive integrated moving average (ARIMA) models, proposed in order to overcome limitations of the traditional ARIMA models; especially data limitation, and yield...

A time series forecasting is an active research applied significantly in a variety of economics areas. Over the past three decades an auto-regressive integrated moving average (ARIMA) model, as one of the most important time series models, has been applied in financial markets forecasting. Recent researches in time series forecasting ARIMA models indicate some basic limitations which detract fr...

Zoonosis refers to the transmission of infectious diseases from animal to human. The increasing number of zoonosis incidence makes the great losses to lives, including humans and animals, and also the impact in social economic. It motivates development of a system that can predict the future number of zoonosis occurrences in human. This paper analyses and presents the use of Seasonal Autoregres...

Ten candidate models of the Auto-Regressive Moving Average (ARMA) family are investigated for representing and forecasting monthly and ten-day streamflow in three Indian rivers. The best models for forecasting and representation of data are selected by using the criteria of Minimum Mean Square Error (MMSE) and Maximum Likelihood (ML) respectively. The selected models are validated for significa...

Understanding the ionospheric variability caused by solar and geomagnetic space weather can be achieved using Total Electron Content (TEC) parameter. Developing a novel artificial intelligence-based machine learning model that forecast TEC parameter would primarily benefit Global Positioning System (GPS) users. This paper holds one such hybrid empirical exploits nonlinear autoregressive neural ...

Since 1990s, many literatures have shown that connectionist models, such as back propagation, recurrent network, and RBF (Radial Basis Function) outperform the traditional models, MA (Moving Average), AR (Auto Regressive), and ARIMA (Auto Regressive Integrated Moving Average) in time series prediction. Neural based approaches to time series prediction require the enough length of historical mea...