The objective of this paper is to develop a novel wind speed forecasting technique, which produces more accurate prediction. The Wavelet Transform (WT) along with the Auto Regressive Moving Average (ARMA) is chosen to form a hybrid whose combination is expected to give minimum Mean Absolute Prediction Error (MAPE). A simulation study has been conducted by comparing the forecasting results using...

Knowing about future values and trend of stock market has attracted a lot of attention by researchers, investors, financial experts and brokers. The benefits of having a good estimation of the stock market behavior are well-known, minimizing the risk of the investment and maximizing the profits. In recent years, mathematical and computational models from artificial intelligence have been used f...

In this paper, we want to examine how unemployment impacts social life, and, by using datasets from six European countries, analyze the effect of on two main aspects life: exclusion and life satisfaction. First, predict rates Auto Regressive Integrated Moving Average (ARIMA) model results are further used in a linear regression alongside satisfaction data, thus obtaining hybrid model. With help...

AIM To study the application of artificial neural network (ANN) in forecasting the incidence of hepatitis A, which had an autoregression phenomenon. METHODS The data of the incidence of hepatitis A in Liaoning Province from 1981 to 2001 were obtained from Liaoning Disease Control and Prevention Center. We used the autoregressive integrated moving average (ARIMA) model of time series analysis ...

FinTech companies have emerged as a new force in the financial industry recent years. Their innovative business models, high growth performance, and broad market prospects attracted attention pursuit of numerous investors. Therefore, predicting future trend company stocks is significant for This paper selects PayPal prediction target, collects closing prices from 2018 to 2023, uses Auto-regress...

successful modeling of hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. on the other hand in training phase of any artificial intelligence (ai) based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole pop...

This paper proposes the combined forecasting model which study on the classic swine fever (CSF) morbidity, using the forecasting results of ARIMA and GM (1, 1) model as the inputs of the majorizing BP neural network. Analyzing the monthly data from 2000 to 2009 and the accuracy of the forecasting results is 97.379%, more accurate and more steady than traditional methods. This research provides ...

Short-term forecast of pertussis incidence is helpful for advanced warning and planning resource needs for future epidemics. By utilizing the Auto-Regressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model as alterative models with R software, this paper analyzed data from Chinese Center for Disease Control and Prevention (China CDC) between January 2005 and June 2...