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The evolution of financial data shows a high degree of volatility of the series, coupled with increasing difficulties of forecasting financial variables. Some alternative forecasting methods, based on the literature review, have been developed, which can be particularly useful in the analysis of financial time series. Despite of the numerous time series forecasting models, the accuracy of time series forecasting is fundamental to many decision processes. Selecting an efficient technique in unique situations is very difficult task for forecasters. Many researchers have integrated linear and nonlinear methods in order to yield more accurate results. In practice, it is difficult to determine the time series under study are generated from a linear or nonlinear underlying process while many aspects of economic behavior may not be pure linear or nonlinear. Although both ARIMA and Artificial Neural Networks (ANNs) models have the flexibility in modeling a variety of problems, none of which is universally the best model used indiscriminately in every forecasting situation. In this paper, based on the foundations of ARIMA and ANNs models, a hybrid method is proposed to forecast exchange rate. Empirical results indicate that integrating linear and nonlinear ARIMA and Artificial Neural Networks (ANNs) models can be an effective way to improve forecasting accuracy achieved by either of the above linear and nonlinear models used separately.

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The forecasting of hydrological variables, such as streamflow, plays an important role in water resource planning and management. Recently, the development of stochastic models is regarded as a major step for this purpose. Streamflow forecasting using the ARIMA model can be conducted when unknown parameters are estimated correctly because parameter estimation is one of the crucial steps in modeling process. The main objective of this research is to explore the performance of parameter estimation methods in the ARIMA model. In this study, four parameter estimation methods have been used: (i) autocorrelation function based on model parameters; (ii) conditional likelihood; (iii) unconditional likelihood; and (iv) genetic algorithm. Streamflow data of Ouromieh River basin situated in Northwest Iran has been selected as a case study for this research. The results of these four parameter estimation methods have been compared using RMSE, RME, SE, MAE and minimizing the sum squares of error. This research indicates that the genetic algorithm and unconditional likelihood methods are, respectively, more appropriate in comparison with other methods but, due to the complexity of the model, genetic algorithm has high convergence to a global optimum.

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Chagan Lake serves as an important ecological barrier in western Jilin. Accurate water quality series predictions for Chagan Lake are essential to the maintenance of water environment security. In the present study, a hybrid AutoRegressive Integrated Moving Average (ARIMA) and Radial Basis Function Neural Network (RBFNN) model is used to predict and examine the water quality [Total Nitrogen (TN), and Total Phosphorus (TP)] of Chagan Lake. The results reveal the following: (1) TN concentrations in Chagan Lake increased slightly from 2006 to 2011, though yearly variations in TP were not significant. The TN and TP levels were mainly classified as Grades IV and V, (2) The hybrid ARIMA and RBFNN model’s RMSE values for the observed and predicted data were 0.139 and 0.036 mg L^-1 for TN and TP, respectively, which indicated that the hybrid model describes TN and TP variations more comprehensively and accurately than single ARIMA and RBFNN model. The results serve as a theoretical basis for ecological and environmental monitoring of Chagan Lake and may help guide irrigation district and water project construction planning for western Jilin Province.

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During past years, economists have been endeavoring to determine both relationship and causality direction between real macroeconomic and nominal economic variables. In this regard, many studies have been carried out on the relation between money and inflation, resulting in the introduction of the notion of money neutrality which implies that permanent change of money supply just affects the nominal variables and has no lasting and real effect on production and employment. Furthermore, even when constant changes of money growth have no real impact whatsoever (except on real monetary equilibriums); money is stated to be super neutral in the long run. Although the majority of economists (with disparate schools of thought) concur with long-term money neutrality, there are still different opinions on the short-term and middle-term neutrality of the money. In following some major of them are presented. This paper investigates the existence of money neutrality in the Iranian economy applying Fisher and Seater approach during 1973 and 2014. The time series analysis, ARIMA model, is used to examine the problem and we consider various monetary aggregates, M1 and M2. Results show that we cannot reject the hypothesis test of money neutrality in Iran. Because all variables are non-stationary and integrated of order one I (1) we can only test the money neutrality. So it is strongly verified that money is neutral and it does not have any significant effects on real non-oil GDP in Iran. Also it was shown that the results are not sensitive to different aggregate money supply.