Comparative analysis of deep learning and classical time series methods to forecast natural gas demand during COVID-19 pandemic
Author:
Affiliation:
1. Faculty of Engineering, Industrial Engineering Department, Samsun University, Samsun, Turkey
Publisher
Informa UK Limited
Subject
Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering
Link
https://www.tandfonline.com/doi/pdf/10.1080/15567249.2023.2241455
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1. Energy Market Prices in Times of COVID-19: The Case of Electricity and Natural Gas in Spain
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4. Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods
5. Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends
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