Hybrid nonlinear adaptive scheme for stock market prediction using feedback FLANN and factor analysis

Anish, C.M.; Majhi, Babita

doi:10.1016/j.jkss.2015.07.002

Cited by 43 publications

(14 citation statements)

References 48 publications

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“…RBF networks have three layers, namely, the input layer, the hidden layer, and the output layer. The input layer is the set of source nodes, the second layer is a high-dimension hidden layer, and the output layer causes the network to respond to applied activation patterns into the input layer [40]. The advantages of the RBF approach include the partial linearity in the parameters and the availability of fast and efficient training methods [41].…”

Section: Radial Basis Function Networkmentioning

confidence: 99%

Exploring the Important Attributes of Human Immunodeficiency Virus and Generating Decision Rules

Cheng

Wang

2020

Symmetry

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Acquired Immunodeficiency Syndrome (AIDS) is the most severe phase of Human Immunodeficiency Virus (HIV) infection. Living with HIV results in a weakened immune system, with AIDS being the final stage of HIV and puzzling the world. The current medical environment remains unable to effectively cure AIDS, with treatment depending on long-term antiretroviral therapy (ART). To effectively treat and prevent HIV, it is important to elucidate the key factors of HIV propagation. This study proposes a rough set classifier based on adding recency (R) (i.e., the last physician visit), frequency (F) (i.e., the frequency of medical visits), and monetary (M) (i.e., medication adherence) attributes and integrated attribute selection methods to generate discriminatory rules and find the core attributes of HIV. The collected data consist of 1308 HIV infection records from Taiwan. From the experimental results, the frequency of CD4+ cells in the peripheral blood is able to determine patient medication, treatment willingness, and HIV infection stages, because HIV patients are less likely to be willing to receive long-term ART. Furthermore, drug abuse is found to be the greatest cause of HIV infection. These results show that the additional RFM attributes can improve classification accuracy, with the core attributes being M, R, plasma viral load (PVL) and age. Hence, we suggest that clinical physicians use these core attributes to understand the HIV infection stages.

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Section: Radial Basis Function Networkmentioning

confidence: 99%

Exploring the Important Attributes of Human Immunodeficiency Virus and Generating Decision Rules

Cheng

Wang

2020

Symmetry

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“…In comparison to LMS based prediction, RLS is more appropriate because of requiring significantly less test to train the model. Table 3 portrays some other enhanced techniques of FLANN from [33][34][35][36]. This model provides a methodology of designing parameters for better performance, thus giving a comparatively a good result.…”

Section: Flannmentioning

confidence: 99%

Applications of neural network based methods on stock market prediction: survey

Mohapatra¹,

Das²,

Das³

et al. 2018

IJET

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Financial forecasting is one of the domineering fields of research, where investor's money is at stake due to the rise or fall of the stock prices which unpredictable and fluctuating. Basically as the demand for stock markets has been rising at an unprecedented rate so its prediction becomes all the more exciting and challenging. Prediction of the forthcoming stock prices mostly Artificial Neural Network (ANN) based models are taken into account. The other models such as Bio-inspired Computing, Fuzzy network model etc., considering statistical measures, technical indicators and fundamental indicators are also explored by the researchers in the field of financial application. Ann's development has led the investors for hoping the best prediction because networks included great capability of machine learning such as classification and prediction. Most optimization techniques are being used for training the weights of prediction models. Currently, various models of ANN-based stock price prediction have been presented and successfully being carried to many fields of Financial Engineering. This survey aims to study the mostly used ANN and related representations on Stock Market Prediction and make a proportional analysis between them.

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“…The RBF Network consists of three layers: input layer, hidden layer, and output layer. The input layer is the set of source nodes, the second layer is a hidden layer high dimension, and the output layer gives the response of the network to the activation patterns applied to the input layer [ 10 ]. The advantages of the RBF approach are the (partial) linearity in the parameters and the availability of fast and efficient training methods [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

“…There are several methods to decide how many factors have to be extracted. The most widely used method for determining the number of factors is using eigenvalues greater than one [ 10 ].…”

Section: Related Workmentioning

confidence: 99%

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

Yang

Cheng

Chan

2017

Computational Intelligence and Neuroscience

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Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

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Hybrid nonlinear adaptive scheme for stock market prediction using feedback FLANN and factor analysis

Cited by 43 publications

References 48 publications

Exploring the Important Attributes of Human Immunodeficiency Virus and Generating Decision Rules

Exploring the Important Attributes of Human Immunodeficiency Virus and Generating Decision Rules

Applications of neural network based methods on stock market prediction: survey

A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

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