Prediction is one of the most important achievements of modeling science, which has a special place in management and decision making. In general, there is a direct relationship between the accuracy of predictions and the quality of made decisions. This is the most important reason why efforts for providing more precise methods of prediction in the subject literature have not stopped despite the existence of numerous methods. The classical AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) models are one of the most important and well-known statistical methods that have been frequently used in various sciences. However, these methods, despite all their unique advantages, have some disadvantages, which sometimes reduce their acceptability. One of the most important of these disadvantages is the limitation of the linearity, the limitation of certainty, the limitation of the number of required data, and the limitation of mixed and multiple structures. Many attempts have been made to address these shortcomings and limitations in the literature. In this paper, a method for overcoming the limitation of complex and multiple structures is presented using the Ensemble Empirical Mode Decomposition (EEMD) techniques. In the proposed method, at first, the under-study time series, which is essentially complex and involves several simultaneous structures, is decomposed into its constituent constituents, which are fundamentally less complicated and include fewer structures. Then, each of these simplified structures is predicted using an AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE model. Ultimately, the prediction of each of the main components is combined to formulate final predictions. The results of applying the proposed method to predict the crude oil price, which is among the most complex time series in financial markets, indicate the effectiveness of the proposed method. Numerical results show that the proposed method can improve the performance of the classic AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE of 65. 57% and 53. 85% in predicting Texas and Brent crude oil prices.

The main purpose of this paper is using the probablity models, Auto REGRESSIVE MOVING AVERAGE (ARMA) in order to modeling of daily position time series of permanent GPS station. The daily position time series of LLAS site in Southern California region from SCIGN array that were active during January 1,2000 to Dec 30, 2006 are evaluated for analysis and determinig of daily position time series. According of daily position time series, a site motion model is used to estimate simultaneously geodetic parameters such as: linear trend, annual harmonics, semi annual harmonics and offsets. In each daily position time series, model parameters are estimated using weighted least squares. In this study, Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) are used as study tools for identification of behavior of daily position time series of permanent GPS station. These functions provide consideration of correlations between daily positions of daily time series. Moreover, Akaike Information Criterion is used to identify model orders, because some kind of ARMA model may appropriate for a daily position time series of GPS station. In this study, some numerical results shows that a model order from (1, 1) is appropriate for direction N of permanent GPS station. Probabality model of ARMA (2, 1) is best model for direction E and a model order from (1, 1) is suitable for direction U. In the final step, a daily position time series of LLAS permanent station were predicted for seasonal component.

Nowadays, electricity load forecasting, as one of the most important areas, plays a crucial role in the economic process. What separates electricity from other commodities is the impossibility of storing it on a large scale and cost-effective construction of new power generation and distribution plants. Also, the existence of seasonality, nonlinear complexity, and ambiguity pattern in electricity data set makes it more difficult to forecast by using the traditional methods. Therefore, new models, computational intelligence and soft computing tools and combining models are the most accurate and widely used methods for modeling the complexity and uncertainty in the data set. In this paper, a parallel optimal hybrid model using computational intelligence tools and soft computations is proposed to forecast the electricity load forecasting. The main idea of this model is the use of the advantages of the individual models in the modeling of complex systems in a structure and elimination of the limitations of them, simultaneously. The experimental results indicate that the proposed hybrid model has a higher performance accuracy in comparison to iterative suboptimal hybrid models and its computational cost is lower than the other hybrid models; also, the proposed model can achieve more accurate results, as compared with its component and some other seasonal hybrid models.

Nowadays, due to the environmental uncertainty and rapid development of new technologies, economic variables are often predicted by using less data and short-term timeframes. Therefore, prediction methods which require fewer amounts of data are needed. Auto REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) model and Artificial Neural Networks (ANNs) need large amounts of data to achieve accurate results, however Fuzzy Regression (FR) models, compared with other models, are more suitable for conditions with less attainable data. In order to solve the above mentioned problem and to achieve more accurate results, in the present paper three methods have been evaluated: Auto REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA), Fuzzy Regression (FR), and Fuzzy Auto REGRESSIVE INTEGRATED MOVING AVERAGE (FARIMA) which is resulted by combining ARIMA and Fuzzy methods. Comparing the accuracy of predictions, based on two criteria RMSE and R2, indicated that Fuzzy Auto REGRESSIVE INTEGRATED MOVING AVERAGE (FARIMA) had the best results in forecasting the price index.

River flow modeling has special importance in water resources management. Since the actual river flow data are often low and they correlate and depend yearly and monthly, making the data similar to historical data is so difficult and complex. In this study, 50 year data and Seasonal Auto REGRESSIVE MOVING AVERAGE (SARMA) and Clayton and Frank Copulas which are the prediction and simulation methods of the river flow molding, were used to generate random flow data of Helmand River. Results show, SARMA model forecasts minimum river flow data very good, but the generated data hasn’t correlation of historical data and usually the maximum river flow is greater than real data. Otherwise, Copula preserved concordance of real data and make the data that are similar to real river flow. Also Root Mean Square Error of Copula method was 0.3 that is was less than SARMA method (0.4). Therefore Copulas are good methods for Helmand river flow modeling.

The quality of traffic flow is one of the main characteristics of the transportation network, which is widely used in issues related to urban planning, route prioritization, reducing traffic congestion and travel time; Therefore, estimating the volume of traffic and predicting it in the future is one of the important issues for transportation planners. The problem of prediction requires modeling and determining the variables affecting changes in a particular phenomenon. In this research, travel demand is predicted using time series methods. The data required for this research have been prepared from the Roads and Transportation Organization. In this study, in order to build a model, two autoREGRESSIVE processes and MOVING AVERAGE have been used. Using the above methods, the amount of demand in the coming years up to the horizon of 1404 on the Qom-Tehran freeway is predicted. The results of the study showed that among the self-correlated and MOVING AVERAGE models and the combination of two models, namely the self-correlated MOVING AVERAGE, the third model has a more acceptable accuracy. The parameters of this model (4,5) ARMA were obtained. Also, the validity of the constructed model, based on the AVERAGE value of absolute error percentage, was 0.047, R and R2 values were calculated 0.94 and 0.89, respectively, which shows that the model has acceptable accuracy.

Switchgrass is known as one of the best second-generation lignocellulosic biomasses for bioethanol production. Designing efficient switchgrass-based bioethanol supply chain (SBSC) is an essential requirement for commercializing the bioethanol production from switchgrass. This paper presents a mixed integer linear programming (MILP) model to design SBSC in which bioethanol demand is under AUTO-REGRESSIVE MOVING AVERAGE (ARMA) time series models. In this paper, how a SBSC design is affected by ARMA time series structure of bioethanol demand is studied. A case study based on North Dakota state in the United States demonstrates application of the proposed approach in designing the optimal SBSC. Moreover, SBSC optimal design is forecasted for the time horizon of 2013 to 2020 with the bioethanol demand acquired from the ARMA models to provide insights for designing and minimizing total cost of SBSC in the future efficiently. Finally, in order to validate the proposed approach, a reproduction behavior test is done. Also, a comparative analysis based on a SBSCND model from the recent literature is elaborated to show the performance of the proposed approach.

The ARIMA model is a precise forecasting model for short time periods, but the limitation of a large amount of historical data is required. However, in our society, due to uncertainty and rapid development of new technology, we usually have to forecast future situations using little data in a short span of time. The historical data must be less than what the ARIMA model employs which limits its application. The fuzzy regression is able to forecast model which is suitable for the uncertain condition and with little attainable historical data. But the results of this model cannot be encouraging because the spread is wide in some cases. The researchers do try to combine the advantages of the fuzzy regression and ARIMA models to formulate the FARIMA model and to overcome the limitations of the fuzzy regression and ARIMA model. Therefore, in this study, a synthetic fuzzy auto REGRESSIVE INTEGRATED MOVING AVERAGE (FARIMA) is employed to forecast crude oil price. The findings show that the proposed method can get more satisfactory results.