Comparative analysis of deep learning and classical time series methods to forecast natural gas demand during COVID-19 pandemic

The lockdown measures implemented to contain the COVID-19 pandemic have had a considerable effect on the consumption of natural gas, which is closely linked to the economic growth of countries. Accurately forecasting natural gas demand is critical for making informed decisions in unprecedented and unexpected situations. This study aims to compare artificial learning-based algorithms and classical statistical time series models in predicting natural gas demand during the pandemic, using Turkey as a case study. Common time series prediction methods, including Autoregressive Integrated Moving Average (ARIMA), Nonlinear Autoregression Neural Network (NARNN), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM), were utilized for this purpose. The impact of the pandemic on natural gas demand was analyzed by including 2-year natural gas consumption data since its onset. Root mean square error (RMSE), correlation coefficient (R), and mean absolute error (MAE) criteria were used as performance evaluation metrics to select the best model. The results confirmed that the deep-learning-based LSTM model provided better prediction accuracy than time-series benchmark models, with the lowest RMSE (9.442) and the highest R (0.997) values in the test dataset. Furthermore, the results were validated by statistical analysis using the Diebold-Mariano and Nemenyi tests.