A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter

Bisoi, Ranjeeta; Dash, P.K.

doi:10.1016/j.asoc.2014.01.039

Cited by 108 publications

(50 citation statements)

References 25 publications

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“…Usually, the clustering is used to develop the model and after the model creation one can classify and predict future behaviour. There is no single hybrid approach instead multiple hybrid approaches are used to find more accurate results [28]. Available work in literature is based on a single data mining techniques; classification or clustering for the prediction of customer churn and mining of retention data of customer [9,22], however, some studies have been conducted which apply more than one technology [2,30].…”

Section: Related Workmentioning

confidence: 99%

Optimizing Coverage of Churn Prediction in Telecommunication Industry

Anjum¹,

Usman²,

Zeb³

et al. 2017

ijacsa

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Abstract-Companies are investing more in analytics to obtain a competitive edge in the market and decision makers are required better identification among their data to be able to interpret complex patterns more easily. Alluring thousands of new customers is worthless if an equal number is leaving. Business Intelligence (BI) systems are unable to find hidden churn patterns for the huge customer base. In this paper, a decision support system has been proposed, which can predict the churning behaviour of a customer efficiently. We have proposed a procedure to develop an analytical system using data mining as well as machine learning techniques C5, CHAID, QUEST, and ANN for the churn analysis and prediction for the telecommunication industry. Prediction performance can be significantly improved by using a large volume and several features from both Business Support Systems (BSS) and Operations Support Systems (OSS). Extensive experiments are performed; marginal increases in predictive performance can be seen by using a larger volume and multiple attributes from both Telco BSS and OSS data. From the results, it is observed that using a combination of techniques can help to figure out a better and precise churn prediction model.

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Section: Related Workmentioning

confidence: 99%

Optimizing Coverage of Churn Prediction in Telecommunication Industry

Anjum¹,

Usman²,

Zeb³

et al. 2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…It can also be considered as an indication of realizing share growth potential [1], [6], [17]. [6], [12][13], [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. The main idea behind using these indicators is to evaluate stock price movements based on historical price patterns and volumes.…”

Section: The Important Variables Used In Predicting Share Performancementioning

confidence: 99%

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Kamley¹,

Jaloree²,

Thakur³

2016

IJECE

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Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts.

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“…In this sense, computations based on fuzzy logic are preferred by some analysts due to its high handling capacity of data uncertainties. Fusion techniques have been proposed like the consolidation of neural networks with fuzzy systems [1]. Some constructs may well include fuzzy neural networks FNN [9], adaptive network fuzzy information system ANFIS [10] or wavelet fuzzy neural networks WLFNN [11].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…Thus, professionals of the financial world have come up with several new ways to make sound trading decisions. Some of these astute computational methods include artificial neural networks (ANN), support vector machines (SVM) and fuzzy logic systems [1]. ANNs variants include the radial basis function neural network RBFNN [2], recurrent neural network RNN [3], multilayer perceptron MLP [4], generalized regression neural networks GRNN [5], random vector functional link neural network FLANN [6], local linear wavelet neural network LLWNN [7] and wavelet neural network WNN [8].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Effects of Technical Market Indicators on Stock Market Index Direction Forecasting

Şenyurt¹,

Subaşı²

2015

Modeling of Artificial Intelligence

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In this study, we examine and compare the performances of data mining techniques based on the daily indexes of five different markets, namely DAX, FTSE 100, NASDAQ Composite, NIKKEI 225 and NYSE Composite. The A/D oscillator, chaikin oscillator, moving average convergence/divergence, stochastic oscillator, acceleration, momentum, chaikin volatility, fast stochastics, slow stochastics, williams %R, negative volume index, positive volume index, relative strength index, accumulation/distribution line, bollinger band, highest high, lowest low, median price, on balance volume, price rate of change, price-volume trend, typical price, volume rate of change, weighted close and williams accumulation/distribution make up the 28 technical indicator attributes used in this study. The data mining techniques utilized in this study comprise of random forest model, artificial neural network (ANN) and support vector machine (SVM). Initially, the input features are sorted with WEKA's Info Ranker which reveals the best attributes based on relative weight. Afterwards, ten attributes having the highest relative weight values are fed into random forest, ANN and SVM. Then different attribute combinations based on their relative weights are tested in turns for more precision.

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A hybrid evolutionary dynamic neural network for stock market trend analysis and prediction using unscented Kalman filter

Cited by 108 publications

References 25 publications

Optimizing Coverage of Churn Prediction in Telecommunication Industry

Optimizing Coverage of Churn Prediction in Telecommunication Industry

Performance Forecasting of Share Market using Machine Learning Techniques: A Review

Effects of Technical Market Indicators on Stock Market Index Direction Forecasting

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