The application of multivariate time series is so large,it can be used in many systems, like ecnomic systems,biological systems, and so on.This paper introduced the method’s building and the structure of ARIMAX model (auto-regressive integrated moving average model with explanatory variables) and its SAS realizing. The paper analysed the tertiaryindustry in China with the realty business to be ...

The need for accurate modeling of rainfall-runoff-sediment processes has grown rapidly in the past decades. This study investigates the efficiency of black-box models including Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average with eXogenous input (ARIMAX) models for forecasting the rainfall-runoff-sediment process. According to the complex behavior of the rainfall-ru...

OBJECTIVES Influence of meteorological variables on the transmission of bacillary dysentery (BD) is under investigated topic and effective forecasting models as public health tool are lacking. This paper aimed to quantify the relationship between meteorological variables and BD cases in Beijing and to establish an effective forecasting model. METHODS A time series analysis was conducted in th...

This paper set out to identify the significant variables which affect residential low voltage (LV) network demand and develop next day total energy use (NDTEU) and next day peak demand (NDPD) forecast models for each phase. The models were developed using both autoregressive integrated moving average with exogenous variables (ARIMAX) and neural network (NN) techniques. The data used for this re...

BACKGROUND The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. METHODS In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 t...

Although judgmental models are widely applied in practice to alleviate the limitation of statistical models ignoring domain knowledge, they are still suffering from many kinds of biases and inconsistencies inherent in subjective judgments. Moreover, most of the prior studies are often concentrated on making judgmental adjustments to statistical projections and ignore incorporating domain knowle...

Given that the traditional ARIMAX model has rarely been applied to any of climate change and environmental agents, which are most cognate agents with associated exogenous variables; neutralize for a better enhanced prediction system, distributional form error term is robust sufficient in capturing accommodating both external covariate(s) high frequency data required. This study therefore evalua...

This paper proposes a technique to implement wavelet analysis (WA) for improving a forecasting accuracy of the autoregressive integrated moving average model (ARIMA) in nonlinear time-series. With the assumption of the linear correlation, and conventional seasonality adjustment methods used in ARIMA (that is, differencing, X11, and X12), the model might fail to capture any nonlinear pattern. Ra...

Wind speed forecasting is difficult not only because of the influence of atmospheric dynamics but also for the impossibility of providing an accurate prediction with traditional statistical forecasting models that work by discovering an inner relationship within historical records. This paper develops a self-adaptive (SA) auto-regressive integrated moving average with exogenous variables (ARIMA...