The U.S. Census Bureau has enhanced the X-12-ARIMA seasonal adjustment program by incorporating an improved automatic regARIMA model (regression model with ARIMA errors) selection procedure. Currently this procedure is available only in test version 0.3 of X-12ARIMA, but it will be released in a future version of the program. It is based on the automatic model selection procedure of TRAMO , an ...

The standardized precipitation index (SPI) was used to quantify the classification of drought in the Guanzhong Plain, China. The autoregressive integrated moving average (ARIMA) models were developed to fit and forecast the SPI series. Most of the selected ARIMA models are seasonal models (SARIMA). The forecast results show that the forecasting power of the ARIMA models increases with the incre...

ARIMA is a popular method to analyze stationary univariate time series data. There are usually three main stages to build an ARIMA model, including model identification, model estimation and model checking, of which model identification is the most crucial stage in building ARIMA models. However there is no method suitable for both ARIMA and SARIMA that can overcome the problem of local optima....

Drought forecasting plays a crucial role in drought mitigation actions. Thus, this research deals with linear stochastic models (autoregressive integrated moving average (ARIMA)) as a suitable tool to forecast drought. Several ARIMA models are developed for drought forecasting using the Standardized Precipitation Evapotranspiration Index (SPEI) in a hyper-arid climate. The results reveal that a...

Measurements of high-speed network traffic have shown that traffic data exhibits a high degree of self-similarity. Traditional traffic models such as AR and ARMA are not able to capture this long-range-dependence making them ineffective for the traffic prediction task. In this paper, we apply the fractional ARIMA (F-ARIMA) model to predict one-step-ahead traffic value at different time scales. ...

This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange. The empirical results obtained reveal the superiority of neural networks model over ARIMA model. The findings further resolve and clarify contradictory opinions reported in literature over the superiority of neural networks and ARIMA mode...