scholarly journals A user-oriented model for Oracles’ Gas price prediction

2021 ◽  
Author(s):  
Giuseppe Antonio Pierro ◽  
Henrique Rocha ◽  
Stéphane Ducasse ◽  
Michele Marchesi ◽  
Roberto Tonelli
Keyword(s):  
Price Prediction ◽  
Gas Price

Natural Gas Price Prediction with Big Data

2019 ◽  
Author(s):  
Yuanyuan Tang ◽  
Qingmei Wang ◽  
Wei Xu ◽  
Mingming Wang ◽  
Zhaowei Wang
Keyword(s):  
Big Data ◽  
Natural Gas ◽  
Price Prediction ◽  
Gas Price

scholarly journals Natural Gas Price Prediction Using Machine Learning

2021 ◽  
Vol 9 (VIII) ◽  
pp. 126-134
Author(s):  
Sanjana G P
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Natural Gas ◽  
Machine Learning Algorithms ◽  
Multilinear Regression ◽  
Random Forest Regression ◽  
Price Prediction ◽  
Proposed Model ◽  
Natural Gas Prices ◽  
Gas Price

Natural gas varies with season. In addition, natural gas supply, demand, storage, and imports are important indicators related to natural gas price. There are plenty of methods for analyzing and forecasting natural gas prices and machine learning is increasingly used. Machine learning algorithms can learn from historical relationships and trends in the data and make data-driven predictions or decisions. Here a new model for predicting price for natural gas by using Machine Learning concepts. Here some algorithms have been used to build the proposed model: Random Forest Regression, Linear Regression, Decision Tree, Multilinear Regression. By using the algorithm, a Flask model has been implemented and tested. The results have been discussed and a full comparison between algorithms was conducted. Random forest Regression was selected as best algorithm based on accuracy.

Monetization of Oil-Gas Price Differentials

2013 ◽  
Author(s):  
Justin Pettit ◽  
Erik Darner ◽  
Mark Jelinek
Keyword(s):  
Price Differentials ◽  
Gas Price ◽  
Oil Gas

TLBO-FLN: Teaching-Learning Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 522-532 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Subhranginee Das ◽  
Mohammad Dilsad Ansari
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Fine Tuning ◽  
Functional Link ◽  
Price Prediction ◽  
Closing Price ◽  
Teaching Learning Based Optimization ◽  
Artificial Neural ◽  
Teaching Learning

Background and Objective: Stock closing price prediction is enormously complicated. Artificial Neural Networks (ANN) are excellent approximation algorithms applied to this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters either leads toward additional computational cost or local optima. Methods: Teaching Learning Based Optimization (TLBO) is a newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of Functional Link Artificial Neural Network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data made it popular and got wide applications in the stock market prediction. This article presents a hybrid model termed as Teaching Learning Based Optimization of Functional Neural Networks (TLBO-FLN) by combining the advantages of both TLBO and FLANN. Results and Conclusion: The model is evaluated by predicting the short, medium, and long-term closing prices of four emerging stock markets. The performance of the TLBO-FLN model is measured through Mean Absolute Percentage of Error (MAPE), Average Relative Variance (ARV), and coefficient of determination (R2); compared with that of few other state-of-the-art models similarly trained and found superior.

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