One of the main tasks to analyze and design a mining system is predicting the behavior exhibited by prices in the future. In this paper, the applications of different prediction methods are evaluated in econometrics and financial management fields, such as ARIMA, TGARCH, and stochastic differential equations, for the time-series of monthly copper prices. Moreover, the performance of these metho...

The formal expression of pro positional attitudes, especially when nested (iterated), is an important problem for A I . An interesting first-order extensional logical system for such expression has been proposed by Creary. In this system concepts (and concepts of concepts, etc.) are made explicit. The system includes "concept functions", which are special functions which act on and deliver conc...

This article reviews the practical aspects of the use of ARIMA (autoregressive, integrated, moving average) modelling of time series as applied to the surveillance of reportable infectious diseases, with special reference to the widely available SSS1 package, produced by the Centers for Disease Control and Prevention. The main steps required by ARIMA modelling are the selection of the time seri...

Autoregressive integrated moving average (ARIMA) is one of the most popular linear models for time series forecasting due to its nice statistical properties and great flexibility. However, its parameters are estimated in a batch manner and its noise terms are often assumed to be strictly bounded, which restricts its applications and makes it inefficient for handling large-scale real data. In th...

The present study aims at developing a forecasting model to predict the next year’s air pollution concentrations in the atmosphere of Iran. In this regard, it proposes the use of ARIMA, SVR, and TSVR, as well as hybrid ARIMA-SVR and ARIMA-TSVR models, which combined the autoregressive part of the autoregressive integrated moving average (ARIMA) model with the support vector regression technique...

The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead pr...

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....

on one hand, oil is the greatest energy resource in the world and, on the other hand, because of the role of oil revenue in the economic of oil producer countries, such as iran,it is vital for these countries. so it is necessary to recognize different affective parameters on oil market for these countries. in this research, we try to forecast oil price as an important variable in world wide oil...