Auto regressive integrated moving average (ARIMA) models have been widely used to calculate monthly time series data formed by interannual variations of monthly data or inter-monthly variation. However, the influence brought about by inter-monthly variations within each year is often ignored. An improved ARIMA model is developed in this study accounting for both the interannual and inter-monthl...

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....

In this study we have analysed wind and wave time series data resulting from hourly measurements on the sea surface in Bushehr, the northern part of the Persian Gulf, from 15 July to 4 August 2000. Wind speed (U10) ranged from 0.34 to 10.38 m/s as alternating sea and land breezes. The lowest wind speed occurs at about midnight and the highest at around noon. The calculated autocorrelation of wi...

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...

This study focuses on predicting and estimating possible stock assets in a favorable real-time scenario for financial markets without the involvement of outside brokers about broadcast-based trading using various performance factors data metrics. Sample from Y-finance sector was assembled API-based series quite accurate precise. Prestigious machine learning algorithmic performances both classif...