scholarly journals An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays

2021 ◽  
Vol 7 ◽  
pp. e409
Author(s):  
Faramarz Saghi ◽  
Mustafa Jahangoshai Rezaee
Keyword(s):  
Time Series ◽  
Natural Gas ◽  
Time Delays ◽  
Wavelet Decomposition ◽  
Vital Role ◽  
Optimal Time ◽  
Price Forecasting ◽  
Fuzzy Transform ◽  
Economic Sectors ◽  
Gas Price

Natural gas, known as the cleanest fossil fuel, plays a vital role in the economies of producing and consuming countries. Understanding and tracking the drivers of natural gas prices are of significant interest to the many economic sectors. Hence, accurately forecasting the price is very important not only for providing an effective factor for implementing energy policy but also for playing an extremely significant role in government strategic planning. The purpose of this study is to provide an approach to forecast the natural gas price. First, optimal time delays are identified by a new approach based on the Euclidean Distance between input and target vectors. Then, wavelet decomposition has been implemented to reduce noise. Moreover, fuzzy transform with different membership functions has been used for modeling uncertainty in time series. The wavelet decomposition and fuzzy transform have been integrated into the preprocessing stage. An ensemble method is used for integrating the outputs of various neural networks. The results depict that the proposed preprocessing methods used in this paper cause to improve the accuracy of natural gas price forecasting and consider uncertainty in time series.

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.

scholarly journals Synchronization Measure Based on a Geometric Approach to Attractor Embedding Using Finite Observation Windows

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Inga Timofejeva ◽  
Kristina Poskuviene ◽  
Maosen Cao ◽  
Minvydas Ragulskis
Keyword(s):  
Time Series ◽  
Time Delay ◽  
Time Delays ◽  
Geometric Approach ◽  
Optimal Time ◽  
Effective Algorithm ◽  
Geometrical Properties ◽  
Finite Observation

A simple and effective algorithm for the identification of optimal time delays based on the geometrical properties of the embedded attractor is presented in this paper. A time series synchronization measure based on optimal time delays is derived. The approach is based on the comparison of optimal time delay sequences that are computed for segments of the considered time series. The proposed technique is validated using coupled chaotic Rössler systems.

Natural Gas Price Forecasting: One Explanation for Upward Bias

1991 ◽  
Vol 9 (3) ◽  
pp. 107-121 ◽  
Author(s):  
Paul A. Ballonoff ◽  
Diana L. Moss
Keyword(s):  
Natural Gas ◽  
Price Forecasting ◽  
Gas Price

Daily natural gas price forecasting by a weighted hybrid data-driven model

2020 ◽  
Vol 192 ◽  
pp. 107240 ◽  
Author(s):  
Jianliang Wang ◽  
Changran Lei ◽  
Meiyu Guo
Keyword(s):  
Natural Gas ◽  
Data Driven ◽  
Price Forecasting ◽  
Hybrid Data ◽  
Gas Price

Natural Gas Price Forecasting in a Changing World

2021 ◽  
Author(s):  
Sara Farhangdoost ◽  
Xiaoli Etienne
Keyword(s):  
Natural Gas ◽  
Price Forecasting ◽  
Gas Price ◽  
Changing World

scholarly journals Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1680 ◽  
Author(s):  
Moting Su ◽  
Zongyi Zhang ◽  
Ye Zhu ◽  
Donglan Zha ◽  
Wenying Wen
Keyword(s):  
Machine Learning ◽  
Natural Gas ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Spot Price ◽  
Support Vector ◽  
Price Forecasting ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Gas Price

Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.

scholarly journals FUTURE NATURAL GAS PRICE FORECASTING MODEL AND ITS POLICY IMPLICATION

2020 ◽  
Vol 10 (5) ◽  
pp. 64-70
Author(s):  
Ambya Ambya ◽  
Toto Gunarto ◽  
Ernie Hendrawaty, ◽  
Fajrin Satria Dwi Kesumah ◽  
Febryan Kusuma Wisnu
Keyword(s):  
Natural Gas ◽  
Policy Implication ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Gas Price

Time series analysis applied to construct US natural gas price functions for groups of states

2010 ◽  
Vol 32 (4) ◽  
pp. 887-900 ◽  
Author(s):  
V.V. Kalashnikov ◽  
T.I. Matis ◽  
G.A. Pérez-Valdés
Keyword(s):  
Time Series ◽  
Natural Gas ◽  
Time Series Analysis ◽  
Series Analysis ◽  
Price Functions ◽  
Gas Price
Sign in / Sign up

Export Citation Format

Share Document