New kernel methods for asset pricing: application to natural gas price prediction

2011 ◽  
Vol 2 (1/2) ◽  
pp. 106 ◽  
Author(s):  
Yinan Hu ◽  
Theodore B. Trafalis
Keyword(s):  
Asset Pricing ◽  
Natural Gas ◽  
Kernel Methods ◽  
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.

Peak-Shaving Ratio Analysis of the Natural Gas Combined Heat and Power Plant With Distributed Peak-Shaving Heat Pumps

2017 ◽  
Author(s):  
Xiling Zhao ◽  
Xiaoyin Wang ◽  
Tao Sun
Keyword(s):  
Natural Gas ◽  
Heat Source ◽  
Heat Pump ◽  
Gas Turbines ◽  
Heat Load ◽  
Operating Cost ◽  
Heat Pumps ◽  
Optimized Design ◽  
Peak Shaving ◽  
Gas Price

Distributed peak-shaving heat pump technology is to use a heat pump to adjust the heat on the secondary network in a substation, with features of low initial investment, flexible adjustment, and high operating cost. The paper takes an example for the system that uses two 9F class gas turbines (back pressure steam) as the basic heat source and a distributed heat pump in the substation as the peak-shaving heat source. The peak-shaving ratio is defined as the ratio of the designed peak-shaving heat load and the designed total heat load. The economic annual cost is taken as a goal, and the optimal peak-shaving ratio of the system is investigated. The influence of natural gas price, electricity price, and transportation distance are also analyzed. It can provide the reference for the optimized design and operation of the system.

scholarly journals News-Based Research on Forecast of International Natural Gas Price Trend

2020 ◽  
Author(s):  
Tianxiang Li ◽  
Xiaosong Han ◽  
Aoqing Wang ◽  
Hui Li ◽  
Guosheng Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Natural Gas ◽  
Predictive Accuracy ◽  
Price Trend ◽  
Learning Network ◽  
Natural Gas Prices ◽  
Deep Learning Network ◽  
Sentiment Score ◽  
Gas Price

In this paper, we build a deep learning network to predict the trends of natural gas prices. Given a time series, for each day, the gas price trend is classified as “up” and “down” according to the price compared to the last day. Meanwhile, we collect news articles as experimental materials from some natural gas related websites. Every article was then embedded into vectors by word2vec, weighted with its sentiment score, and labeled with corresponding day’s price trend. A CNN and LSTM fused network was then trained to predict price trend by these news vectors. Finally, the model’s predictive accuracy reached 62.3%, which outperformed most of other traditional classifiers.

Sign in / Sign up

Export Citation Format

Share Document