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    Tourist arrival forecasting by evolutionary fuzzy systems

    , Article Tourism Management ; Volume 32, Issue 5 , 2011 , Pages 1196-1203 ; 02615177 (ISSN) Hadavandi, E ; Ghanbari, A ; Shahanaghi, K ; Abbasian Naghneh, S ; Sharif University of Technology
    Accurate forecasts of tourist arrivals and study of the tourist arrival patterns are essential for the tourism-related industries to formulate efficient and effective strategies on maintaining and boosting tourism industry in a country. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. This study presents a hybrid artificial intelligence (AI) model to develop a Mamdani-type fuzzy rule-based system to forecast tourist arrivals with high accuracy. The hybrid model uses genetic algorithm for learning rule base and tuning data base of fuzzy system. Actually it extracts useful information patterns with a descriptive rule induction approach based... 

    Large metropolitan water demand forecasting using DAN2, FTDNN, and KNN models: A case study of the city of Tehran, Iran

    , Article Urban Water Journal ; Volume 14, Issue 6 , 2017 , Pages 655-659 ; 1573062X (ISSN) Ghiassi, M ; Fa'al, F ; Abrishamchi, A ; Sharif University of Technology
    Taylor and Francis Ltd  2017
    Efficient operation of urban water systems necessitates accurate water demand forecasting. We present daily, weekly, and monthly water demand forecasting using dynamic artificial neural network (DAN2), focused time-delay neural network (FTDNN), and K-nearest neighbor (KNN) models for the city of Tehran. The daily model investigates whether partitioning weekdays into weekends and non-weekends can improve forecast results; it did not. The weekly model yielded good results by using the summation of the daily forecast values into their corresponding weeks. The monthly results showed that partitioning the year into high and low seasons can improve forecast accuracy. All three models offer very... 

    Developing a time series model based on particle swarm optimization for gold price forecasting

    , Article Proceedings - 3rd International Conference on Business Intelligence and Financial Engineering, BIFE 2010, 13 August 2010 through 15 August 2010, Hong Kong ; August , 2010 , Pages 337-340 ; 9780769541167 (ISBN) Hadavandi, E ; Ghanbari, A ; Abbasian Naghneh, S ; Sharif University of Technology
    The trend of gold price in the market is the most important consideration for the investors of the gold, and serves as the basis of gaining profit, so there are scholars who try to forecast the gold price. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. Besides, nowadays artificial intelligence (AI) techniques are becoming more and more widespread because of their accuracy, symbolic reasoning, flexibility and explanation capabilities. Among these techniques, particle swarm optimization (PSO) is one of the best AI techniques for optimization and parameter estimation. In this study a PSO-based time series model for the gold price... 

    An adaptive neural network-fuzzy linear regression approach for improved car ownership estimation and forecasting in complex and uncertain environments: The case of Iran

    , Article Transportation Planning and Technology ; Volume 35, Issue 2 , 2012 , Pages 221-240 ; 03081060 (ISSN) Azadeh, A ; Neshat, N ; Rafiee, K ; Zohrevand, A. M ; Sharif University of Technology
    This paper applies a novel adaptive approach consisting of Artificial Neural Network (ANN) and Fuzzy Linear Regression (FLR) to improve car ownership forecasting in complex, ambiguous, and uncertain environments. This integrated approach is applied to forecast car ownership in Iran from 1930 to 2007. In this study, the level of car ownership is viewed as the result of demographic, politico-social, and urban structure factors including average family size, total population density, urban population density, urbanization rate, gross national product per capita, gasoline price, and total road length. To capture the potential complexity, uncertainty, and linearity relation between the car... 

    Developing an evolutionary neural network model for stock index forecasting

    , Article Communications in Computer and Information Science, 18 August 2010 through 21 August 2010 ; Volume 93 CCIS , August , 2010 , Pages 407-415 ; 18650929 (ISSN) ; 3642148301 (ISBN) Hadavandi, E ; Ghanbari, A ; Abbasian Naghneh, S ; Sharif University of Technology
    The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques and combining them to improve forecasting accuracy in different fields. Besides, stock market forecasting has always been a subject of interest for most investors and professional analysts. Stock market forecasting is a tough problem because of the uncertainties involved in the movement of the market. This paper proposes a hybrid artificial intelligence model for stock exchange index forecasting, the model is a combination of genetic algorithms and feedforward neural networks. Actually it evolves neural network weights by using genetic algorithms. We also employ preprocessing... 

    Forecasting models for flow and total dissolved solids in Karoun river-Iran

    , Article Journal of Hydrology ; Volume 535 , 2016 , Pages 148-159 ; 00221694 (ISSN) Salmani, M. H ; Salmani Jajaei, E ; Sharif University of Technology
    Water quality is one of the most important factors contributing to a healthy life. From the water quality management point of view, TDS (total dissolved solids) is the most important factor and many water developing plans have been implemented in recognition of this factor. However, these plans have not been perfect and very successful in overcoming the poor water quality problem, so there are a good volume of related studies in the literature. We study TDS and the water flow of the Karoun river in southwest Iran. We collected the necessary time series data from the Harmaleh station located in the river. We present two Univariate Seasonal Autoregressive Integrated Movement Average (ARIMA)... 

    Machine learning in energy economics and finance: A review

    , Article Energy Economics ; Volume 81 , 2019 , Pages 709-727 ; 01409883 (ISSN) Ghoddusi, H ; Creamer, G. G ; Rafizadeh, N ; Sharif University of Technology
    Elsevier B.V  2019
    Machine learning (ML) is generating new opportunities for innovative research in energy economics and finance. We critically review the burgeoning literature dedicated to Energy Economics/Finance applications of ML. Our review identifies applications in areas such as predicting energy prices (e.g. crude oil, natural gas, and power), demand forecasting, risk management, trading strategies, data processing, and analyzing macro/energy trends. We critically review the content (methods and findings) of more than 130 articles published between 2005 and 2018. Our analysis suggests that Support Vector Machine (SVM), Artificial Neural Network (ANN), and Genetic Algorithms (GAs) are among the most... 

    A new semi-analytical modeling of steam-assisted gravity drainage in heavy oil reservoirs

    , Article Journal of Petroleum Science and Engineering ; Volume 69, Issue 3-4 , 2009 , Pages 261-270 ; 09204105 (ISSN) Alali, N ; Pishvaie, M. R ; Jabbari, H ; Sharif University of Technology
    Thermal recovery by steam injection has proven to be an effective means of recovering heavy oil. Forecasts of reservoir response to the application of steam are necessary before starting a steam drive project. Thermal numerical models are available to provide forecasts. However, these models are expensive and consume a great deal of computer time. An alternative to numerical modeling is to use a semi-analytical model. The objective of the current study was to investigate thermal applications of horizontal wells for displacement and gravity drainage processes using analytical modeling as well as reservoir simulation. The main novelties presented in the paper are: a) the transient temperature... 

    Application of soft computing models in streamflow forecasting

    , Article Proceedings of the Institution of Civil Engineers: Water Management ; Volume 172, Issue 3 , 2019 , Pages 123-134 ; 17417589 (ISSN) Adnan, R. M ; Yuan, X ; Kisi, O ; Yuan, Y ; Tayyab, M ; Lei, X ; Sharif University of Technology
    ICE Publishing  2019
    The accuracy of five soft computing techniques was assessed for the prediction of monthly streamflow of the Gilgit river basin by a cross-validation method. The five techniques assessed were the feed-forward neural network (FFNN), the radial basis neural network (RBNN), the generalised regression neural network (GRNN), the adaptive neuro fuzzy inference system with grid partition (Anfis-GP) and the adaptive neuro fuzzy inference system with subtractive clustering (Anfis-SC). The interaction between temperature and streamflow was considered in the study. Two statistical indexes, mean square error (MSE) and coefficient of determination (R2), were used to evaluate the performances of the...