Fuzzy artificial neural network (p, d, q) model for incomplete financial time series forecasting

Khashei, Mehdi; Bijari, Mehdi

doi:10.3233/ifs-130775

Cited by 70 publications

(58 citation statements)

References 76 publications

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“…The second category is the combinational or hybrid algorithms such as fuzzy inference systems. In this category, algorithms of the neural networks are considered as adaptive fuzzy systems that may operate on fuzzy numbers instead of crisp values [15].…”

Section: Ann Model Selectionmentioning

confidence: 99%

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Artificial and Hybrid Fuzzy Linear Neural Network‐Based Estimation of Seed Oil Content of Safflower

Sabzalian¹,

Khashei²,

Ghaderian³

2014

J Americ Oil Chem Soc

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Keywords Safflower · Oil content · Multiple linear regression · Neural networks · Hybrid fuzzy linear neural network methods of quantitative analysis used by the NIR system are multiple linear regression (MLR), principal component regression (PCR), partial least squares (PLS), and recently artificial neural networks (ANN). The first three methods of MLR, PCR and PLS are suitable for linear analysis, while ANN has advantages when non-linearity exists in the data. Rodriguez-Nogales [2], compared PLS, PCR and MLR to find optimal prediction models for the quantification of Abstract 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 obtained interpolation results of MLR had desired accuracy, the extrapolation was extremely poor. The extrapolation determination coefficient (R 2 ) and standard error (SE) of cross validation for MLR models were 0.63-0.78 and 3.71-4.44, respectively. In order to overcome the accuracy limitation of linear MLR models, a common suggestion is to use a nonlinear artificial neural network (ANN); however, it needs a large number of data to yield significant accurate results. We developed a novel robust hybrid fuzzy linear neural (HFLN) network to capture simultaneously linear and nonlinear patterns of data with a limited number of safflower samples. Empirical extrapolation results showed that the HFLN had higher R 2 (=0.85) and lower SE (=1.83) compared to those obtained by MLR and ANN models. It is concluded that hybrid methodologies could be used to construct efficient and appropriate models for estimation of seed oil content set up on NIR system.

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Section: Ann Model Selectionmentioning

confidence: 99%

“…The last category is the evolutionary or heuristic algorithms. In these algorithms, the optimal architecture is searched over topology space by varying the number of hidden layers and neurons, simultaneously, according to a previously specified objective function [15].…”

Section: Ann Model Selectionmentioning

confidence: 99%

Artificial and Hybrid Fuzzy Linear Neural Network‐Based Estimation of Seed Oil Content of Safflower

Sabzalian¹,

Khashei²,

Ghaderian³

2014

J Americ Oil Chem Soc

Self Cite

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show abstract

“…In proposed model, instead of using crisp, fuzzy parameters in the form of triangular fuzzy numbers are used for related parameters of layers (w i;j i50; 1; 2; …; p ð j51; 2; …; qÞ, w j j50; 1; 2; …; q ð Þ ). The model is described using a fuzzy function with a fuzzy parameter [52]:…”

Section: Formulation Of the Hybrid Proposed Model (Fmlp)mentioning

confidence: 99%

A bi‐level neural‐based fuzzy classification approach for credit scoring problems

et al. 2013

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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 granting credits to customers instead of intuitive experience or portfolio management. Accuracy is one of the most important criteria in order to choose a credit‐scoring model; and hence, the researches directed at improving upon the effectiveness of credit scoring models have never been stopped. In this article, a hybrid binary classification model, namely FMLP, is proposed for credit scoring, based on the basic concepts of fuzzy logic and artificial neural networks (ANNs). In the proposed model, instead of crisp weights and biases, used in traditional multilayer perceptrons (MLPs), fuzzy numbers are used in order to better model of the uncertainties and complexities in financial data sets. Empirical results of three well‐known benchmark credit data sets indicate that hybrid proposed model outperforms its component and also other those classification models such as support vector machines (SVMs), K‐nearest neighbor (KNN), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA). Therefore, it can be concluded that the proposed model can be an appropriate alternative tool for financial binary classification problems, especially in high uncertainty conditions. © 2013 Wiley Periodicals, Inc. Complexity 18: 46–57, 2013

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“…Its wide utilization is because of the few distinctive highlights of ANNs that make them alluring to the two specialists and modern professionals. As expressed in [4], ANNs are information-driven, self-versatile techniques with not many earlier suspicions. They are likewise great indicators with the capacity to summed up objective facts from the outcomes gained from unique information, along these lines allowing right surmising of the inert piece of the populace.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Techniques for Sales Prediction

Reddy*,

Kumar,

Kanth

et al. 2020

IJRTE

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All of us are very curious about future, very excited to know what will happen in the next moment. Similarly, retailers are also curious about the future of their business, its progress and their future sales. Walmart is the world’s biggest retailer and also has a vast grocery chain over the world. It was initially established in America 1962. In 2019, it has more than 11,000 stores in 28 countries but the sales differ from place to place. Many sales strategies, discount rates will be introduced for the improvement of sales. Retailers always try to attract the common people to visit their store. They always focus on improving the future sales. Using some Machine learning forecasting models, we can estimate the future sales based on the past data. Our aim is to apply time series forecasting models to retail sales data, which contains weekly sales of 45 Walmart stores across United States from 2010 to 2012. There are other factors which effects the analysis of weekly sales - markdown, consumer per index, Is Holiday (boolean value returns whether it is holiday or not), size of the store, unemployment, store type, fuel price and temperature. The forecasting models applied for the data are Autoregressive Integrated Moving Average (ARIMA) model and Feed Forward Neural Networks (FFNN). The dataset will be divided into training and testing datasets. The predicted values will be checked with the test data and accuracy will be calculated. Based on the accuracy we conclude which of the two models will better for the sales prediction.

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Fuzzy artificial neural network (p, d, q) model for incomplete financial time series forecasting

Cited by 70 publications

References 76 publications

Artificial and Hybrid Fuzzy Linear Neural Network‐Based Estimation of Seed Oil Content of Safflower

Artificial and Hybrid Fuzzy Linear Neural Network‐Based Estimation of Seed Oil Content of Safflower

A bi‐level neural‐based fuzzy classification approach for credit scoring problems

Forecasting Techniques for Sales Prediction

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