Mehdi Khashei

Mehdi Khashei
Isfahan University of Technology | IUT · Department of Industrial Engineering

About

73
Publications
18,172
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2,823
Citations
Citations since 2016
50 Research Items
2080 Citations
20162017201820192020202120220100200300400
20162017201820192020202120220100200300400
20162017201820192020202120220100200300400
20162017201820192020202120220100200300400
Introduction

Publications

Publications (73)
Article
Purpose The purpose of this paper is to propose a new linear-nonlinear data preprocessing-based hybrid model to achieve a more accurate result at a lower cost for wind power forecasting. For this purpose, a decomposed based series-parallel hybrid model (PKF-ARIMA-FMLP) is proposed which can model linear/nonlinear and certain/uncertain patterns in u...
Article
Full-text available
It is essential to prevent gas pipeline leakages to protect the environment and avoid financial losses and casualties, especially in densely populated areas. For half a century, in gas distribution networks, polyethylene pipes with advantages, including corrosion resistance properties, ease of implementation, and lower operation cost, are considere...
Article
Full-text available
The complexity of real-world time series makes to hardly yield the desired prediction performance by the existing individual models. A series hybrid model that relies on decomposing time series and then sequentially modeling patterns have been frequently adopted in this domain. Nevertheless, a lack of capturing all existing patterns, including all...
Article
Full-text available
Achieving the desired accuracy in time series forecasting has become a binding domain, and developing a forecasting framework with a high degree of accuracy is one of the most challenging tasks in this area. Combining different forecasting methods to construct efficient hybrid models has been widely reported in the literature regarding this challen...
Article
Full-text available
Background Coronavirus outbreak (SARS-CoV-2) has become a serious threat to human society all around the world. Due to the rapid rate of disease outbreaks and the severe shortages of medical resources, predicting COVID-19 disease severity continues to be a challenge for healthcare systems. Accurate prediction of severe patients plays a vital role i...
Article
Accurate forecasting of real-world systems becomes a highly challenging task due to the inherent complexity of time series modeling. Hybrid models have been successfully applied to deal with such problems and yield desired forecasting accuracy. The fundamental objective of hybridization is to exploit the unite modeling benefits of every single mode...
Article
Hybridization of individual models emerged as a predominant alternative for increasing accuracy in time series forecasting. The literature is abundant on providing hybrid methods aiming at improving forecasting accuracy and comprehensive pattern recognition. The principle behind all hybrid models' success is improving forecasting accuracy. One of t...
Article
Full-text available
Abstract Wind power is one of the most important renewable energy sources that is widely used in many developed and developing countries. However, it is generally stated in the literature that providing accurate forecasts for large‐scale planning purposes is not a simple task, especially by single models. It is the main reason for this fact that wh...
Article
Forecasting in the medical domain is critical to the quality of decisions made by physicians, patients, and health planners. Modeling is one of the most important components of decision support systems, which are frequently used to simulate and analyze under-studied systems in order to make more appropriate decisions in medical science. In the medi...
Article
In recent years, the idea of using a mathematical model to describe the behavior of physical phenomena has been very much considered. Specifically, a definitive model, based on physical laws, enables researchers to calculate the number of time dependencies precisely at any moment in time. However, in the real world, we often face time-dependent phe...
Article
Full-text available
Support vector machines (SVMs) are one of the most popular and widely used approaches in modeling. Various kinds of SVM models have been developed in the literature of prediction and classification in order to cover different purposes. Fuzzy and crisp support vector machines are a well-known branch of modeling approaches frequently applied for cert...
Article
Background and aims In recent decades, modeling and forecasting have played a significant role in the diagnosis and treatment of different diseases. Various forecasting models have been developed to improve data-based decision-making processes in medical systems. Although these models differ in many aspects, they all originate from the assumption t...
Preprint
Full-text available
Modeling and forecasting are among the most powerful and widely-used tools in decision support systems. The Fuzzy Linear Regression (FLR) is the most fundamental method in the fuzzy modeling area in which the uncertain relationship between the target and explanatory variables is estimated and has been frequently used in a broad range of real-world...
Article
Purpose Improving the accuracy and reducing computational costs of predictions, especially the prediction of time series, is one of the most critical parts of the decision-making processes and management in different areas and organizations. One of the best solutions to achieve high accuracy and low computational costs in time series forecasting is...
Preprint
Full-text available
The financial markets have always witnessed the competition of their participants for gaining high and stable profits. The realization extent of this goal depends on the profitability of the trading points or turning points (TPs) ahead. TPs prediction problem is one of the most challenging yet important problems in the financial discipline. The fir...
Article
Regression modeling is one of the most widely used statistical processes to estimate the relationships between dependent and independent variables, which have been frequently applied in a wide range of applications successfully. This method includes many techniques for modeling and analyzing several variables to cover real-world problems. The perfo...
Article
Purpose The purpose of this paper is to propose an extensiveness intelligent hybrid model to short-term load electricity forecast that can simultaneously model the seasonal complicated nonlinear uncertain patterns in the data. For this purpose, a fuzzy seasonal version of the multilayer perceptrons (MLP) is developed. Design/methodology/approach I...
Article
Purpose The purpose of this paper, because of the complexity of demand time series and the need to construct a more accurate hybrid model that can model all relationships in data, is to propose a parallel-series hybridization of seasonal neural networks and statistical models for demand time series forecasting. Design/methodology/approach The main...
Article
Wind power is one of the most important clean energy and alternative to fossil fuels. More attention has been paid to this renewable resource in today’s world due to increasing public awareness, concerns about greenhouse gas emissions and environmental issues, and reducing the oil and gas reservoirs. Accurate and precise wind speed and wind power f...
Article
Full-text available
Forecasting spare parts requirements is a challenging problem, because the normally intermittent demand has a complex nature in patterns and associated uncertainties, and classical forecasting approaches are incapable of modeling these complexities. The present study introduces a hybrid model that can impressively overcome the limitations of classi...
Conference Paper
With the increasing importance of forecasting with the high degree of accuracy, many forecasting approaches have been broadly developed to forecast in an accurate way. Series hybrid methodology is one of the most commonly-used hybrid approaches that has encountered a great amount of attractiveness in the literature of time series forecasting and ha...
Article
The accuracy and risk of electricity load forecasting are the most critical features, which play a significant role in efficient management, future economic planning, and decision making by financial and operational decision-makers of generation and distribution powers. It is the main reason for providing more comprehensive models to increase the a...
Preprint
Full-text available
Support vector machines (SVMs) are one of the most popular and widely-used approaches in modeling. Various kinds of SVM models have been developed in the literature of prediction and classification in order to cover different purposes. Fuzzy and crisp support vector machines are a well-known branch of modeling approaches that frequently applied for...
Article
Full-text available
Literature indicates that many efforts have been conducted toward the development of forecasting models with a high degree of accuracy. Combining different models is known as a powerful alternative to access more reliable and more accurate results than single models. Given the great importance of hybridization theory, various hybrid models have bee...
Article
With the increasing importance of forecasting with the utmost degree of accuracy, utilizing hybrid frameworks become a must for obtaining more accurate and more reliable forecasting results. Series hybrid methodology is one of the most widely-used hybrid approaches that has encountered a great amount of popularity in the literature of time series f...
Conference Paper
ترکیب مدلهای مختلف از پرکاربردترین استراتژی های ممکن به منظور بهبود دقت پیشبینی های مالی است. در حالت کلی تکنیک های ترکیب مدلهای تکی را میتوان به دو دسته سری و موازی تقسیم بندی نمود. هدف نهایی تمامی این روشها استفاده از ویژگیهای خاص هر یک از مدلهای تکی در مدل سازی ساختارهای پیچیده موجود در داده ها و به ویژه بهبود دقت پیشبینی ها است. با وجود مزایای...
Article
Full-text available
With the importance of forecasting with a high degree of accuracy, the increasing attention has been evolved to combine individual models, especially statistical and intelligent ones. The main aim of such that hybrid models is to extract unique modeling strengths in linear and nonlinear pattern recognition, respectively. Therefore, different hybrid...
Conference Paper
ترکیب مدلهاي مختلف یک استراتژي مؤثر به منظور بهبود دقت پیشبینیهاست. یکی از مهمترین شاخههاي مدلهاي ترکیبی ارائه شده در ادبیات مو ضوع مربوط به ترکیب موازي مدلهاي کلا سیک آماري و هوش محا سباتی ا ست. در این روش ترکیب مقادیر پیشبینی شده تو سط هر یک از مدلهاي تکی با استفاده از روشهاي وزندهی، بهصورت خطی با یکدیگر ترکیب شده و پیشبینیهاي نهایی حاصل میشوند....
Conference Paper
بهکارگیری ترکیب روشهای متفاوت به منظور دستیابی به نتایج دقیقتر میباشند. استفاده از مدلهای ترکیبی یا ترکیب مدلهای مختلف یک راهحل معمول بهمنظور بهبود دقت پیشبینی است. در ادبیات موضوع پیشبینی سریهای زمانی، ترکیب مدلهای خطی کلاسیک و غیرخطی هوشمند یکی از معمولترین روشهای ترکیبی به منظور بهبود دقت پیشبینی میباشد. مدلهای میانگین متحرک خودرگرسیون انباشته و...
Conference Paper
یکی از مهمترین چالشهایی که اساساً فراروي مدیران و تصمیمگیرندگان وجود داشته ودر اغلب موارد تصمیمات آنان را دچار مشکل مینماید، نبود اطلاعات کافی و عدم قطعیت موجود در مسائل دنیاي واقع میباشد. امروزه این کمبود کمی ویا کیفی اطلاعات و دادههاي مورد نیاز، سبب شده تا علیرغم وجود روشهاي متعدد پیشبینی، هنوز حصول نتایج دقیق کار چندان سادهاي نباشد.الگوهاي مربوط...
Article
Purpose This study aims to make investment decisions in stock markets using forecasting-Markowitz based decision-making approaches. Design/methodology/approach The authors’ approach offers the use of time series prediction methods including autoregressive, autoregressive moving average and artificial neural network, rather than calculating the exp...
Article
The key factor in selecting appropriate forecasting model is accuracy. Given the deficiencies of single models in processing various patterns and relationships latent in data, hybrid approaches have been known as promising techniques to achieve more accurate results for time series modeling and forecasting. Therefore, a rapid development has been e...
Article
فراهم آوردن داده‌هاي مورد نياز به منظور ارائه پيش‌بيني‌هاي دقيق با شبکه عصبي مصنوعي در صنعت نساجي، اصولاً بسيار هزينه‌بر و زمان‌بر است. از اين رو، استفاده از روش‌هايي که قادر به ارائه پيش‌بيني با تعداد داده‌هاي قابل حصول کم هستند، در اين‌گونه از صنايع مناسب‌تر‌ و کارآمدتر خواهد بود. در اين مقاله، از ترکيب روش‌هاي شبکه‌هاي عصبي مصنوعي و رگرسيون فازي...
Article
Full-text available
فراهم آوردن داده‌هاي مورد نياز به منظور ارائه پيش‌بيني‌هاي دقيق با شبکه عصبي مصنوعي در صنعت نساجي، اصولاً بسيار هزينه‌بر و زمان‌بر است. از اين رو، استفاده از روش‌هايي که قادر به ارائه پيش‌بيني با تعداد داده‌هاي قابل حصول کم هستند، در اين‌گونه از صنايع مناسب‌تر‌ و کارآمدتر خواهد بود. در اين مقاله، از ترکيب روش‌هاي شبکه‌هاي عصبي مصنوعي و رگرسيون فازي...
Article
The key factor in selecting appropriate forecasting model is accuracy. Given the deficiencies of single models in processing various patterns and relationships latent in data, hybrid approaches have been known as promising techniques to achieve more accurate results for time series modeling and forecasting. Therefore, a rapid development has been e...
Article
Full-text available
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...
Article
Over the past few decades, a large literature has evolved through time series forecasting via combining different individual models by employing various hybrid strategies in order to improve forecasting accuracy. One of the most attractive and extensively-used methodologies, proposed in the literature of time series forecasting is the series method...
Article
Full-text available
Financial crises in banking systems are due to inability to manage credit risks. Credit scoring is one of the risk management techniques that analyze the borrower's risk. In this paper, using the advantages of computational intelligence as well as soft computing methods, a new hybrid approach is proposed in order to improve credit risk management....
Article
Combining different models is one of the most well established strategies in the literature in order to capture different patterns of the data, lifting limitations of single models, and improve forecasting accuracy. In recent decades, various hybrid models have been developed by combining different models such as linear/nonlinear, supervised/unsupe...
Article
Foreign exchange rates are among the most important economic indices in the international monetary markets. For large multinational firms, which conduct substantial currency transfers in the course of business, being able to accurately forecast movements of currency exchange rates can result in substantial improvement in the overall profitability o...
Article
Despite several individual forecasting models that have been proposed in the literature, accurate forecasting is yet one of the major challenging problems facing decision makers in various fields, especially financial markets. This is the main reason that numerous researchers have been devoted to develop strategies to improve forecasting accuracy....
Article
Consumption forecasting is a critical issue in commodity markets on which financial decision-makers depend for accuracy. To adequately handle the complexity and uncertainty associated with real-world market problems, forecasting needs to be capable of handling complex situations. The steel industry is a strategic one for Iran playing a critical rol...
Article
Series hybrid models are one of the most widely-used hybrid models that in which a time series is assumed to be composed of two linear and nonlinear components. In this paper, the performance of two types of these hybrid models is evaluated for predicting stock prices in order to introduce the more reliable series hybrid model. For this purpose, AR...
Article
Combining different models is one of the most well established strategies in the literature in order to capture different patterns of the data, lifting limitations of single models, and improve forecasting accuracy. In recent decades, various hybrid models have been developed by combining different models such as linear/nonlinear, supervised/unsupe...
Article
Full-text available
Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers. Given its direct impact on related decisions, various attempts have been made to achieve more accurate and reliable forecasting results, of which the combining of individual models remains a widely appl...
Article
Forecasting methods are one of the most efficient available approaches to make managerial decisions in various fields of science. Forecasting is a powerful approach in the planning process, policy choices and economic performance. The accuracy of forecasting is an important factor affects the quality of decisions that generally has a direct non-str...
Article
Cluster analysis or clustering is one of the most important and widely used techniques for data exploration and knowledge discovery that concerned with partitioning a set of objects in such a way that objects in the same groups, called clusters, are more similar to each other than to those in other clusters. However, obtaining the clusters that exh...
Article
Full-text available
Risk management is one of the most important branches of business and finance. Classification models are the most popular and widely used analytical group of data mining approaches that can greatly help financial decision makers and managers to tackle credit risk problems. However, the literature clearly indicates that, despite proposing numerous c...
Article
In today’s world, using quantitative methods are very important for financial markets forecast, improvement of decisions and investments. In recent years, various time series forecasting methods have been proposed for financial markets forecasting. In each case, the accuracy of time series methods fundamental to make decision and hence the research...
Article
Inexpensive and rapid methods for measurement of seed oil content by near infrared reflectance spectroscopy (NIRS) are useful for developing new oil seed cultivars. Adopting default multiple linear regression (MLR), the predictions of safflower oil content were made by 20-140 samples using a Perten Inframatic 8620 NIR spectrometer. Although the obt...
Article
Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the hybridization are quite d...
Article
The credit scoring is a risk evaluation task considered as a critical decision for financial institutions in order to avoid wrong decision that may result in huge amount of losses. Classification models are one of the most widely used groups of data mining approaches that greatly help decision makers and managers to reduce their credit risk of gran...
Article
Full-text available
Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Fuzzy autoregressive integrated moving average (FARIMA) models are the fuzzy improved version of the autoregressive integrated moving average (ARIMA) models, proposed in order to overcome limitations of the traditional ARIM...
Article
Feed-forward neural networks (FFNNs) are among the most important neural networks that can be applied to a wide range of forecasting problems with a high degree of accuracy. Several large-scale forecasting competitions with a large number of commonly used time series forecasting models conclude that combining forecasts from more than one model ofte...
Article
Autoregressive integrated moving average (ARIMA) models are one of the most important time series models applied in financial market forecasting over the past three decades. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indi...
Article
Seasonal autoregressive integrated moving average (SARIMA) models form one of the most popular and widely used seasonal time series models over the past three decades. However, in several researches it has been argued that they have two basic limitations that detract from their popularity for seasonal time series forecasting tasks. SARIMA models as...
Article
Applying quantitative models for forecasting and assisting investment decision making has become more indispensable in business practices than ever before. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integra...
Article
The classification problem of assigning several observations into different disjoint groups plays an important role in business decision making and many other areas. Developing more accurate and widely applicable classification models has significant implications in these areas. It is the reason that despite of the numerous classification models av...
Article
Improving time series forecasting accuracy is an important yet often difficult task. Both theoretical and empirical findings have indicated that integration of several models is an effective way to improve predictive performance, especially when the models in combination are quite different. In this paper, a model of hybrid artificial neural networ...
Article
Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing decision makers in many areas. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in combination...
Article
Full-text available
Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one...
Article
Classification is an important data mining task that widely used in several different real world applications. In microarray analysis, classification techniques are applied in order to discriminate diseases or to predict outcomes based on gene expression patterns, and perhaps even to identify the best treatment for given genetic signature. The most...
Article
Full-text available
Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of forecasting problems with a high degree of accuracy. However, using ANNs to model linear problems have yielded mixed results, and hence; it is not wise to apply them blindly to any type of data. This is the reason t...
Article
Full-text available
Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forec...
Article
Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Auto-Regressive Integrated Moving Average (ARIMA) models are one of the most important time series models used in financial market forecasting over the past three decades. Recent research activities in time series forecas...
Article
Quantitative methods have nowadays become very important tools for forecasting purposes in financial markets as for improved decisions and investments. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method; hence, never has research directed at improving upon the effectiveness of time series models sto...
Article
Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting models proposed in several past decades, it is widely recognized that exchange rates are extremely difficult to...

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