Design and analysis of experiments in ANFIS modeling for stock price prediction

Alizadeh, Meysam; Gharakhani, Mohsen; Fotoohi, Elnaz; Rada, Roy

doi:10.5267/j.ijiec.2011.01.001

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…When implementing ANFIS the articles cited in the references were used [55,56]. All of the methods have been executed in "Mathworks MATLAB R2019" software.…”

Section: Results and Evaluationmentioning

confidence: 99%

A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine

Sedighi

Jahangirnia

Gharakhani³

et al. 2019

Data

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This paper intends to present a new model for the accurate forecast of the stock’s future price. Stock price forecasting is one of the most complicated issues in view of the high fluctuation of the stock exchange and also it is a key issue for traders and investors. Many predicting models were upgraded by academy investigators to predict stock price. Despite this, after reviewing the past research, there are several negative aspects in the previous approaches, namely: (1) stringent statistical hypotheses are essential; (2) human interventions take part in predicting process; and (3) an appropriate range is complex to be discovered. Due to the problems mentioned, we plan to provide a new integrated approach based on Artificial Bee Colony (ABC), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Support Vector Machine (SVM). ABC is employed to optimize the technical indicators for forecasting instruments. To achieve a more precise approach, ANFIS has been applied to predict long-run price fluctuations of the stocks. SVM was applied to create the nexus between the stock price and technical indicator and to further decrease the forecasting errors of the presented model, whose performance is examined by five criteria. The comparative outcomes, obtained by running on datasets taken from 50 largest companies of the U.S. Stock Exchange from 2008 to 2018, have clearly demonstrated that the suggested approach outperforms the other methods in accuracy and quality. The findings proved that our model is a successful instrument in stock price forecasting and will assist traders and investors to identify stock price trends, as well as it is an innovation in algorithmic trading.

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“…When implementing ANFIS the articles cited in the references were used [55,56]. All of the methods have been executed in "Mathworks MATLAB R2019" software.…”

Section: Results and Evaluationmentioning

confidence: 99%

A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine

Sedighi

Jahangirnia

Gharakhani³

et al. 2019

Data

Self Cite

View full text Add to dashboard Cite

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“…Various versions of ANN are developed to leverage its characteristic of capturing non-linearity efficiently such as ANN with exogenous input (Kummong and Supratid, 2016) and quasiperiodic ANN to forecast industrial average return (Bodyanskiy and Popov, 2006). Similarly, adaptive ANN (Wong et al, 2010) and the hybrid adaptive neuro-fuzzy inference system is also observed to perform better than the conventional ANN (Alizadeh et al, 2011). If we talk about the existing literature on container throughput forecasting, ARIMA + ANN is observed to be very promising (Tseng et al, 2002;Wang and Jiang, 2019;Xiao et al, 2012;Zhang, 2003).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…However, further study revealed that most of the time series is non-linear in nature and the method proposed by Box and Jenkins was not able to capture the dynamics of time series efficiently (Clements et al, 2004). Therefore, ANN has emerged very promising to account for the non-linearity in the time series (Alizadeh et al, 2011). Various versions of ANN are developed to leverage its characteristic of capturing non-linearity efficiently such as ANN with exogenous input (Kummong and Supratid, 2016) and quasiperiodic ANN to forecast industrial average return (Bodyanskiy and Popov, 2006).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Forecasting container throughput with long short-term memory networks

Shankar¹,

Ilavarasan

Punia

et al. 2019

IMDS

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Purpose Better forecasting always leads to better management and planning of the operations. The container throughput data are complex and often have multiple seasonality. This makes it difficult to forecast accurately. The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods. Design/methodology/approach In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput. The container throughput data of the Port of Singapore are used for empirical analysis. The forecasting performance of the LSTM model is compared with seven different time-series forecasting methods, namely, autoregressive integrated moving average (ARIMA), simple exponential smoothing, Holt–Winter’s, error-trend-seasonality, trigonometric regressors (TBATS), neural network (NN) and ARIMA + NN. The relative error matrix is used to analyze the performance of the different models with respect to bias, accuracy and uncertainty. Findings The results showed that LSTM outperformed all other benchmark methods. From a statistical perspective, the Diebold–Mariano test is also conducted to further substantiate better forecasting performance of LSTM over other counterpart methods. Originality/value The proposed study is a contribution to the literature on the container throughput forecasting and adds value to the supply chain theory of forecasting. Second, this study explained the architecture of the deep-learning-based LSTM method and discussed in detail the steps to implement it.

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“…The algorithm for a network with a single hidden layer is depicted in Fig. 2 Determining the number of nodes in the hidden layer is important because the neural network's mapping accuracy and ability to generalize from training data notablydepends on the number of units in hidden layer (Schalkoff, 1997;Alizadeh et al, 2011). A network with small hidden units cannot completely identify the organization in the training data.…”

Section: Neural Network(multi-layer Perceptron)mentioning

confidence: 99%

Considering supply risk for supplier selection using an integrated framework of data envelopment analysis and neural networks

Nourbakhsh¹,

Ahmadi²,

Mahootchi³

2013

ijiec

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For many years, supplier selection as an important multi-criteria decision has attracted both the researchers and practitioners. During recent years, high incidence of natural disasters, terrorism attacks, labor strikes and other kinds of risks, also known as disruptions, indicates the vulnerability of procurement process to these unpredicted events. In this study, a new framework is introduced to select suppliers while considering the supply risks. In the proposed framework, an expert determines the reliabilities of procurement elements (i.e., production, transportation and communication) based on some proposed risk factors. Then, a trained Multi-Layer Perceptron (MLP) network plays the role of the expert opinion for estimating the reliability scores. In addition to reliabilities, the Data Envelopment Analysis (DEA) is used to take into account the conventional selection criteria: price, delivery, quality and capacity. A set of Pareto-optimal suppliers is obtained from the combination of efficiencies and reliability scores. Finally, the decision maker chooses between the non-dominated suppliers. Obtained experiment results indicate the effectiveness of the proposed framework.

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Design and analysis of experiments in ANFIS modeling for stock price prediction

Cited by 12 publications

References 27 publications

A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine

A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine

Forecasting container throughput with long short-term memory networks

Considering supply risk for supplier selection using an integrated framework of data envelopment analysis and neural networks

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