Clustering and Regression Techniques for Stock Prediction

Bini, B.S.; Mathew, Tessy

doi:10.1016/j.protcy.2016.05.104

Cited by 46 publications

(18 citation statements)

References 3 publications

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“…8 Data mining adopts techniques from many domains (Han et al 2012) Multiple regression is a technique for modeling the association among the scalar dependent variable V and one or more descriptive variables indicated by Y. It predicts the future value of the variable with respect to other variables.V = w 0 + w 1 y 1 + ⋯ + w n y n + where V implies the dependent variable, w 0 -w n implies the coefficients, y 1 -y n implies the independent variables, and є implies the random error (Bini et al 2016).…”

Section: Regressionmentioning

confidence: 99%

Novel approach for predicting water alternating gas injection recovery factor

Belazreg

Mahmood

Aulia

2019

J Petrol Explor Prod Technol

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Water alternating gas (WAG) injection process is a proven EOR technology that has been successfully deployed in many fields around the globe. The performance of WAG process is measured by its incremental recovery factor over secondary recovery. The application of this technology remains limited due to the complexity of the WAG injection process which requires time-consuming in-depth technical studies. This research was performed for a purpose of developing a predictive model for WAG incremental recovery factor based on integrated approach that involves reservoir simulation and data mining. A thousand reservoir simulation models were developed to evaluate WAG injection performance over waterflooding. Reservoir model parameters assessed in this research study were horizontal and vertical permeabilities, fluids properties, WAG injection scheme, fluids mobility, trapped gas saturation, reservoir pressure, residual oil saturation to gas, and injected gas volume. The outcome of the WAG simulation models was fed to the two selected data mining techniques, regression and group method of data handling (GMDH), to build WAG incremental recovery factor predictive model. Input data to the machine learning technique were split into two sets: 70% for training the model and 30% for model validation. Predictive models that calculate WAG incremental recovery factor as a function of the input parameters were developed. The predictive models correlation coefficient of 0.766 and 0.853 and root mean square error of 3.571 and 2.893 were achieved from regression and GMDH methods, respectively. GMDH technique demonstrated its strength and ability in selecting effective predictors, optimizing network structure, and achieving more accurate predictive model. The achieved WAG incremental recovery factor predictive models are expected to help reservoir engineers perform quick evaluation of WAG performance and assess a WAG project risk prior launching detailed time-consuming and costly technical studies.

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Section: Regressionmentioning

confidence: 99%

Novel approach for predicting water alternating gas injection recovery factor

Belazreg

Mahmood

Aulia

2019

J Petrol Explor Prod Technol

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“…He explored how fuzzy logic, coupled with an android application, and Artificial Intelligence Techniques to predict the stock market movement can be very effective in some case. Izzah (2017) building on the work of Bini (2016) for using data mining techniques (regression and classification) to predict stock market, developed a mobile app for stock prediction using improved multiple linear regression. This paper is an attempt to develop a mobile app for stock prediction using LSTM model.…”

Section: Related Workmentioning

confidence: 99%

Integrated Mobile Application Based on Machine Learning for East Africa Stock Market

Manishimwe

Alexander

Kaluuma

et al. 2021

J INFORM SYSTEMS ENG

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Regional Integration refers to the process of states coming together to sign agreements where they agree to cooperate in certain areas common to them. The East African Community (EAC) which was founded on November 30, 1999, identified three integration pillars; common Market protocol, customs union and political federation. In attempt to achieve the named pillars, financial markets have been identified as a key driver for common market and customs union. The challenge however is that the East African countries run the most expensive stocks on the African Continent and this has greatly influenced trade volumes and investor appetite. When these markets are integrated it allows collective bargaining thereby increasing their market share across the region other than a given country running its own exchange. This paper presents a software platform that will enable an investor to purchase and sell stocks irrespective of any location in the East African Region thus spur the development of stock market in terms of volume and competitiveness.

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“…Multiple regression is a technique for modeling the association among the scalar dependent variable V and one or more descriptive variables indicated by Y. It predicts the future value of the variable with respect to other variables.V = w 0 + w 1 y 1 + ⋯ + w n y n + , where V implies the dependent variable, w 0 to w n implies the coefficients, y 1 to y n implies the independent variables, and ε implies the random error (Bini and Mathew 2016).…”

Section: Regressionmentioning

confidence: 99%

Water alternating gas incremental recovery factor prediction and WAG pilot lessons learned

Belazreg

Mahmood

2019

J Petrol Explor Prod Technol

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Water alternating gas (WAG) injection process is a proven enhanced oil recovery (EOR) technology with many successful field applications around the world. WAG pilot projects demonstrate that WAG incremental recovery factor typically ranges from 5 to 10% of original oil in place, though up to 20% has been observed in some fields. Despite its proven success, WAG application growth has been very slow. One of the reasons is the unavailability of robust analytical predictive tools that could estimate WAG incremental recovery factor, which is required for preliminary economic analysis before committing to expensive and time-consuming detailed technical studies and field pilot test that often requires a lot of input data. A seminumerical model for WAG incremental recovery factor prediction was developed based on data mining of published WAG pilots to fill this gap. An extensive review of published WAG pilot projects was carried out, and consequently, 33 projects from 28 fields around the world were selected for this research study. Field WAG incremental recovery factor and parameters with total of one hundred and seventy-seven (177) observations were inputted to the predictive model. A predictive model was developed using both regression and group method of data handling (GMDH) techniques; 70% of the 33 WAG pilot projects data were used as validation, whereas remaining 30% of data set were used for validation. The predictive model results achieved with coefficient of determination (R 2) from the regression method were ranging from 0.892 to 0.946 and 0.854 to 0.917 for training and validation sets, respectively. However, the prediction model coefficient of determination (R 2) using GMDH method was ranging from 0.964 to 0.981 and 0.934 to 0.974 for training and validation, respectively. The developed predictive model can predict WAG incremental recovery factor versus multiple input parameters that include rock type, WAG process type, hydrocarbon pore volume of injected gas, reservoir permeability, oil gravity, oil viscosity, reservoir pressure, and reservoir temperature. The results of the study demonstrated that few input parameters have a significant impact on WAG incremental recovery factor as reservoir permeability and hydrocarbon pore volume of injected gas. This research study uses a novel approach in pre-defining the expected incremental WAG recovery factor before committing resources for building complex numerical reservoir simulation models and running WAG pilot tests, which are very time-consuming and costly and require extensive data input.

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Clustering and Regression Techniques for Stock Prediction

Cited by 46 publications

References 3 publications

Novel approach for predicting water alternating gas injection recovery factor

Novel approach for predicting water alternating gas injection recovery factor

Integrated Mobile Application Based on Machine Learning for East Africa Stock Market

Water alternating gas incremental recovery factor prediction and WAG pilot lessons learned

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