Abstract

The COVID-19 virus outbreak paralyzed travel and mobility in Malaysia, as with other countries across the globe. Mobility through air transport constitutes 12.5% of its transport sector or 0.5% of its gross domestic output without the mobility disruption. As with global air statistics, aviation in- dustry of Malaysia suffered throughout the pandemic. This paper uses the deep learning model Long Short Term Memory (LSTM) in forecasting the recovery period of the country’s aviation industry. Results suggest that the number of flights to and from Malaysia rebound to its pre-pandemic level within four to five months after the containment of the virus.

Keywords: LSTM; Deep learning; Big data; COVID-19; Aviation

JEL Codes: C5; C53; Z30