A modified neural network model for predicting the crude oil price

Mahdiani, Mohammad Reza; Khamehchi, Ehsan

doi:10.1016/j.intele.2017.02.001

Cited by 34 publications

(11 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Step 5 Training accuracy can be measured by using equation (13) and (14). Check for optimal parameters If not optimal then goto step 6.…”

Section: A Working Processmentioning

confidence: 99%

“…So Mean Square error of the forecasting representation can be stated as in equation (13). The feat of projected reproduction can be evaluated by equation (14) and the parameters used are shown in table 4. The proposed illustration is estimated by subsequent equations.…”

Section: B Presentation Assessmentmentioning

confidence: 99%

“…[12][13] used some stock market data in south korean stock and product costs in ANN based semisupervised model by not paying concentration to the historical data of the time series being expected and accomplished superior outcome contrast to their challenger by using only historical data of the objective. [13][14] suggested a model that supportive in projecting composite associations in between unexpected changes of financial data. An additional dimension of research is time gap of forecasting [15] talk about the significance of data regularity in sampling.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advanced Deep Learning Framework for Stock Value Prediction

Das¹,

Mishra²

2019

IJITEE

View full text Add to dashboard Cite

The main attractive feature to stock market is speedy growth of stock economic value in short yoke of time. The investor analyses the demonstration, estimated value and growth of organizations before investing money in market. The analysis may not be enough by using conventional process or some available methods suggested by different researches. In present days large number of stocks are available in market it is very difficult to study each stock by help of very few suggested foretelling methods. To know the anticipated stock value we need some advanced prediction technology for stock market. This paper introduce an advanced skillful method to plan and analyze the different organizers stock execution in market and prognosticate best suitable stock by predicting close price of stock. The projected arrangement is based on multilayer deep learning neural Network optimized by Adam optimizer. Recent 6 years (2010-2016) data of different organizations are applied to the model to demonstrate the skillfulness of the projected proficient method. From result it has been ascertained that the projected framework is best suited to all different data set of various sectors. The prediction error is very minimal as visible from outcome graph of framework

show abstract

“…Step 5 Training accuracy can be measured by using equation (13) and (14). Check for optimal parameters If not optimal then goto step 6.…”

Section: A Working Processmentioning

confidence: 99%

Section: B Presentation Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advanced Deep Learning Framework for Stock Value Prediction

Das¹,

Mishra²

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…Furthermore, many of the recently most cited articles within O & G research employ ANN. That is the case of Ahmadi [129] (production performance), Mahdiani and Khamehchi [130] (crude oil price), Mirzaei-Paiaman et al [131] (production flow rate), Torabi et al [132] (physical properties), and Arjun and Aneesh [133] (physical properties), to name a few. However, using ANN to study the bullwhip effect on the oil and gas industry is still not common.…”

Section: Application To Oil and Gas Industrymentioning

confidence: 99%

Using Machine Learning for Enhancing the Understanding of Bullwhip Effect in the Oil and Gas Industry

Sousa

Ribeiro²,

Relvas

et al. 2019

MAKE

View full text Add to dashboard Cite

Several suppliers of oil and gas (O & G) equipment and services have reported the necessity of making frequent resources planning adjustments due to the variability of demand, which originates in unbalanced production levels. The occurrence of these specific problems for the suppliers and operators is often related to the bullwhip effect. For studying such a problem, a research proposal is herein presented. Studying the bullwhip effect in the O & G industry requires collecting data from different levels of the supply chain, namely: services, upstream and midstream suppliers, and downstream clients. The first phase of the proposed research consists of gathering the available production and financial data. A second phase will be the statistical treatment of the data in order to evaluate the importance of the bullwhip effect in the oil and gas industry. The third phase of the program involves applying artificial neural networks (ANN) to forecast the demand. At this stage, ANN based on different training methods will be used. Further on, the attained mathematical model will be used to simulate the effects of demand fluctuations and assess the bullwhip effect in an oil and gas supply chain.

show abstract

“…Thus, a correct and proficient prediction instrument is crucial for crude oil price forecast. Advances in computational intelligence techniques including artificial neural networks (ANNs) are used as better alternative to this domain [2]. ANN-based models are applied successfully to forecast the crude oil price [3 -6].…”

Section: Introductionmentioning

confidence: 99%

A fireworks algorithm based Pi-Sigma neural network (FWA-PSNN) for modelling and forecasting chaotic crude oil price time series

Nayak¹

2018

EAI Endorsed Transactions on Energy Web

View full text Add to dashboard Cite

Capturing the complex correlations among the data on the crude oil price time series is challenging, hence accurate prediction of it is difficult. Contrast to multilayer artificial neural network, Pi-Sigma neural network (PSNN) is characterized with stronger approximation ability, fast learning and higher fault tolerance. Fireworks algorithm (FWA) is a new metaheuristic motivated from the occurrence of fireworks explosion, characterized with fast convergence capability, parallelism and finds global optima. This article intends to achieve the synergetic effect of both on hybridizing them. It uses FWA for optimization of weight and bias of PSNN (FWA-PSNN) and overcomes the limitations of gradient based training. It has single hidden layer structure and trainable weight, hence fast and robust. The FWA-PSNN is evaluated on prediction of crude oil price time series. Extensive simulation results, performance analysis, and statistical significance test suggested the suitability of FWA-PSNN.

show abstract

A modified neural network model for predicting the crude oil price

Cited by 34 publications

References 15 publications

Advanced Deep Learning Framework for Stock Value Prediction

Advanced Deep Learning Framework for Stock Value Prediction

Using Machine Learning for Enhancing the Understanding of Bullwhip Effect in the Oil and Gas Industry

A fireworks algorithm based Pi-Sigma neural network (FWA-PSNN) for modelling and forecasting chaotic crude oil price time series

Contact Info

Product

Resources

About