a b s t r a c tEnergy consumption is on the rise in developing economies. In order to improve present and future energy supplies, forecasting energy demands is essential. However, lack of accurate and comprehensive data set to predict the future demand is one of big problems in these countries. Therefore, using ensemble hybrid forecasting models that can deal with shortage of data set could be a suitable solution. In this paper, the annual energy consumption in Iran is forecasted using 3 patterns of ARIMA-ANFIS model. In the first pattern, ARIMA (Auto Regressive Integrated Moving Average) model is implemented on 4 input features, where its nonlinear residuals are forecasted by 6 different ANFIS (Adaptive Neuro Fuzzy Inference System) structures including grid partitioning, sub clustering, and fuzzy c means clustering (each with 2 training algorithms). In the second pattern, the forecasting of ARIMA in addition to 4 input features is assumed as input variables for ANFIS prediction. Therefore, four mentioned inputs beside ARIMA's output are used in energy prediction with 6 different ANFIS structures. In the third pattern, due to dealing with data insufficiency, the second pattern is applied with AdaBoost (Adaptive Boosting) data diversification model and a novel ensemble methodology is presented.The results indicate that proposed hybrid patterns improve the accuracy of single ARIMA and ANFIS models in forecasting energy consumption, though third pattern, used diversification model, acts better than others and model's MSE criterion was decreased to 0.026% from 0.058% of second hybrid pattern. Finally, a comprehensive comparison between other hybrid prediction models is done. IntroductionEnergy is vital important for development of every country from the social, economic and environmental perspective. It has magnificent effect on industrial and agricultural products, health, sanitary, population, education and human life quality [1].As energy is a crucial input to industrial part of country, energy demand increases along the industrial function increase. Rapid changes in industry and economy strongly affect energy consumption. Therefore, energy consumption is an important economical index that represents economic development of a city or a country [2]. According to the international energy agent report, there should be many transformations in amount and type of future energy consumption (year 2030). As over the past decade global energy consumption has increased rapidly because of population and economic growth [3,4]. According to wide growth of energy consumption in the last decade, energy demand management is very important for achieving economic success, environment preservation and suitable planning for existing resources that result in self-sufficiency and economic development. Therefore, various techniques have been used for energy demand management to forecast future energy demands accurately [4]. However, energy forecasting is difficult, because it is affected by rapid development of economy, technology, gove...

Forecasting stock returns and their risk represents one of the most important concerns of market decision makers. Although many studies have examined single classifiers of stock returns and risk methods, fusion methods, which have only recently emerged, require further study in this area. The main aim of this paper is to propose a fusion model based on the use of multiple diverse base classifiers that operate on a common input and a Meta classifier that learns from base classifiers' outputs to obtain more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes is determined using a methodology based on dataset clustering and candidate classifiers' accuracy. The results demonstrate that Bagging exhibited superior performance within the fusion scheme and could achieve a maximum of 83.6% accuracy with Decision Tree, LAD Tree and Rep Tree for return prediction and 88.2% accuracy with BF Tree, DTNB and LAD Tree in risk prediction. For feature selection part, a wrapper-GA algorithm is developed and compared with the fusion model. This paper seeks to help researcher select the best individual classifiers and fuse the proper scheme in stock market prediction. To illustrate the approach, we apply it to Tehran Stock Exchange (TSE) data for the period from 2002 to 2012.

In today’s competitive environment, holding companies are usually unable to successfully compete in production of goods and services due to technological sophistication. Therefore, for success of holding companies, selecting appropriate strategic alliance partner is a critical factor. Accordingly, the aim of the paper is to propose a systematic approach for an effective partner selection. Firstly, the underlying motivation and reasons for a strategic alliance building are presented using a SWOT analysis. Criteria of partners’ evaluation are attained on the basis of combining strengths, weaknesses, opportunities and threats. Due to uncertainty of criteria, they are weighted using fuzzy quantitative strategic planning matrix (FQSPM). Because of a large number of criteria obtained from the SWOT-FQSPM analysis, criteria are diminished based on their weights using the Gap analysis with fuzzy data ranking. In the next step, it is proposed to apply four ranking algorithms including the Fuzzy Additive Ratio Assessment (ARAS-F), the Fuzzy Complex Proportional Assessment (COPRAS-F), the Fuzzy Multi-Objective Optimization by Ratio Analysis (Fuzzy MOORA), and the Fuzzy Technique for Order Preference by Similarity to Ideal solution (Fuzzy TOPSIS) to evaluate strategic partners. Finally, the results are combined with the help of the Borda method to choose the best alternative. To illustrate the efficiency of the proposed approach, a real partner selection problem at a holding industries factory in Iran is presented.