Similar Search Results

Hints of Earlier and Other Creation : Unsupervised Machine Learning in Financial Time-Series Analysis

Chen, James Ming; Agiropoulos, Charalampos - 2023

This study extends previous work applying unsupervised machine learning to commodity markets. "Clustering Commodity Markets in Space and Time" [DOI: 10.1016/j.resourpol.2021.102162] examined returns and volatility in commodity markets. That paper supported the conventional ontology of commodity...

Persistent link: https://www.econbiz.de/10014356740

Demand for storage of natural gas in Northwestern Europe : a simulation based forecast 2006-2030

Höffler, Felix (contributor); Kübler, Madjid (contributor) - 2006

Persistent link: https://www.econbiz.de/10003322368

Forecasting Natural Gas Demand in China : Logistic Modelling Analysis

Shaikh, Faheemullah - 2016

-population model based approach to forecast the medium- (2020) to long- (2035) term natural gas demand in China. The adopted modelling … forecasting results show that China's natural gas demand will reach 330–370 billion m3 in the medium-term and 500–590 billion m3 … and international institutions and scholars. The growing natural gas demand will cause significant increase in import …

Persistent link: https://www.econbiz.de/10012987423

Forecasting natural gas consumption using Bagging and modified regularization techniques

Meira, Erick; Oliveira, Fernando Luiz Cyrino; De … - In: Energy economics 106 (2022), pp. 1-23

Persistent link: https://www.econbiz.de/10013202112

A sigmoid regression and artificial neural network models for day-ahead natural gas usage forecasting

Ravnik, Jure; Jovanovac, Josip; Trupej, Alexander; … - In: Cleaner and responsible consumption 3 (2021), pp. 1-13

Reliable and accurate day-ahead forecasting of natural gas consumption is vital for the operation of the Energy sector. Three different forecasting models are developed in this paper: The sigmoid function regression model, the feed-forward neural network, and the recurrent neural network model....

Persistent link: https://www.econbiz.de/10014226853