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Predicting oil price movements: A dynamic Artificial Neural Network approach
, Article Energy Policy ; Vol. 68, issue , 2014 , p. 371-382 ; Amiri, R. M ; Talaei, A ; Jamasb, T ; Sharif University of Technology
Abstract
Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to...
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) ; Ghanbari, A ; Abbasian Naghneh, S ; Sharif University of Technology
2010
Abstract
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 intelligent ACO-SA approach for short term electricity load prediction
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 18 August 2010 through 21 August 2010 ; Volume 6216 LNAI , 2010 , Pages 623-633 ; 03029743 (ISSN) ; 9783642149313 (ISBN) ; Hadavandi, E ; Abbasian Naghneh, S ; Huang D. S ; Zhang X ; Sharif University of Technology
2010
Abstract
Intelligent solutions, based on artificial intelligence (AI) technologies, to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. On the other hand, electrical load prediction is one of the important concerns of power systems so development of intelligent prediction tools for performing accurate predictions is essential. This study presents an intelligent hybrid approach called ACO-SA by hybridization of Ant Colony Optimization (ACO) and Simulated Annealing (SA). The hybrid approach consists of two general stages. At the first stage time series inputs will be fed into ACO and it performs a global search to find a globally optimum solution....
Estimating the step-change time of the location parameter in multistage processes using MLE
, Article Quality and Reliability Engineering International ; Volume 28, Issue 8 , 2012 , Pages 843-855 ; 07488017 (ISSN) ; Niaki, S. T. A ; Sharif University of Technology
2012
Abstract
In this paper, maximum likelihood step-change point estimators of the location parameter, the out-of-control sample and the out-of-control stage are developed for auto-correlated multistage processes. To do this, the multistage process and the concept of change detection are first discussed. Then, a time-series model of the process is presented. Assuming step changes in the location parameter of the process, next, the likelihood functions of different samples before and after receiving out-of-control signal from an X-bar control chart were derived under different conditions. The maximum likelihood estimators were then obtained by maximizing the likelihood functions. Finally, the accuracy and...
A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation
, Article Expert Systems with Applications ; Volume 36, Issue 8 , 2009 , Pages 11108-11117 ; 09574174 (ISSN) ; Saberi, M ; Gitiforouz, A ; Saberi, Z ; Sharif University of Technology
2009
Abstract
This paper presents a hybrid adaptive network based fuzzy inference system (ANFIS), computer simulation and time series algorithm to estimate and predict electricity consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and complex estimation and forecasting. Computer simulation is developed to generate random variables for monthly electricity consumption. Various structures of ANFIS are examined and the preferred model is selected for estimation by the proposed algorithm. Finally, the preferred ANFIS and time series...
Pattern recognition in financial surveillance with the ARMA-GARCH time series model using support vector machine
, Article Expert Systems with Applications ; Volume 182 , 2021 ; 09574174 (ISSN) ; Akhavan Niaki, S. T ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
As the intersection of finance and statistics, financial surveillance is a new interdisciplinary field of research. In this field, statistical process control methods are applied to monitor financial indices. The final aim is to detect out-of-control conditions and trigger a signal as soon as possible. These early signals can help practitioners in making on-time decisions. In this paper, a new method based on a support vector machine is proposed to detect upward and downward shifts with step and trend patterns in auto-correlated financial processes. These processes are modeled by the autoregressive moving average (ARMA) and generalized autoregressive conditional heteroskedasticity (GARCH)...