A new hybrid artificial neural networks and fuzzy regression model for time series forecasting

Khashei, Mehdi; Hejazi, Seyed Reza; Bijari, Mehdi

doi:10.1016/j.fss.2007.10.011

Cited by 179 publications

(87 citation statements)

References 56 publications

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“…In another level, features are used to produce diversity among classifiers, in a way each classifier uses a subset of features [10], [14], [5]. In a different look, different kinds of classifiers are used in combination to make the diverse [11].…”

Section: Figure 2 Training Phase Of Bagging Methodsmentioning

confidence: 99%

Increasing Classifier Ensemble Efficiency using KSBC Algorithm

Nogorabi¹,

Sajedi²

2013

IJCA

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The most challenging issue in constructing a classification system based on ensemble is how to construct an appropriate ensemble of basic classifiers. In this paper, a new approach of constructing ensemble namely, KSBC, K-means Based Classifier Selection is introduced. This approach utilizes Bagging algorithm as the producer of basic classifiers. Type of all basic classifiers, decision tree or multi-layer nervous networks are considered and remained unchanged during construction of ensemble. After constructing a large number of basic classifiers, KSBC partitions them with the help of kmeans clustering. Afterward, by choosing one classifier from each partition, final ensemble is constructed. Weight voting method is the assembling function of the ensemble. In addition the approaches for selection of a classifier from each partition were analyzed. The effectiveness of sampling rate on the efficiency of combining classification based on clustering of classifiers was also evaluated. Finally, several tests were carried out on a large number of standard datasets in machine learning database. Experimental results illustrate the effectiveness of the proposed method compared to other approaches.

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Section: Figure 2 Training Phase Of Bagging Methodsmentioning

confidence: 99%

Increasing Classifier Ensemble Efficiency using KSBC Algorithm

Nogorabi¹,

Sajedi²

2013

IJCA

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“…The decision boundaries could be modified in real-time using new data as they become available, and could be implemented using artificial hardware "'neurons" that operate entirely in parallel. It was found that PNN paradigm was 200,000 times faster than back-propagation; from the neural network model by Frank H. F. Leung,2003 [6] both the input-output relationships of an application and the network structure using the improved GA could be learnt; A new hybrid artificial neural networks and fuzzy regression model for time series forecasting was proposed by Mehdi Khasheiet al,2008 [7]; A Unified Artificial Neural Network Architecture for Active Power Filters, Abdeslam, D.O,2007 [8], models for the prediction of surface roughness in electrical discharge machining Angelos P. Markopoulos et al,2008 [9],the prediction of performance and exhaust emissions in SI engine using ethanol-gasoline blends M. KianiDehKianiet al,2010 [10].…”

Section: Related Workmentioning

confidence: 99%

Impact of Advertisements on Educational Institutions Admission using Classifier

Ashok¹,

Apoorva²,

Suganthi³

2015

IJCA

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Advertisements play a very important role in making educational institutions reach students belonging to rural and urban areas. The purpose of this paper is to find the impact of various types of advertisements on the area wise admission of students to Institutions, both in rural and urban areas of Bangalore. The objective is also to study, among the types, which has sustained or temporary influence. The type of advertisements used are television, handbills, social media, text messaging. The mining technique used is neural network which is considered to be a very good classifier. It is found that television and handbill have sustained impact; text messaging has temporary impact and social media and websites have no impact, in rural area. Television, Social media, and website have sustained, text messaging has temporary, and handbills have no impact in urban areas. Hence this study will help the institutions in adopting a novel strategy where in a particular or amalgamation of advertising can be chosen to effectively increase the popularity of institutions and hence admissions.

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“…Azimi et al [28] built a novel hybrid model to forecast the short-term electrical load, because a single model cannot figure out the characteristics of the time series data. Khashei and Bijari [29] considered that there was no a single model that could ensure the real process of the data generation. Shukur and Lee [30] proposed a hybrid model, including ANN and auto regressive integrated moving average (ARIMA), taking full advantage of the linear and non-linear advantages of the two models.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Dong

et al. 2016

Energies

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Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people's livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

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A new hybrid artificial neural networks and fuzzy regression model for time series forecasting

Cited by 179 publications

References 56 publications

Increasing Classifier Ensemble Efficiency using KSBC Algorithm

Increasing Classifier Ensemble Efficiency using KSBC Algorithm

Impact of Advertisements on Educational Institutions Admission using Classifier

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

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