Imputation recovery tourism demand forecasting

Tourism demand forecasting is vital for planning yet faces challenges like data misalignment and external shocks. Data misalignment—due to missing values, irregular reporting, and inconsistent frequencies—undermines data quality. Shocks such as pandemics or natural disasters disrupt patterns, reducing the reliability of historical data. Traditional models (e.g., ARIMA, ETS) assume clean, regular data, limiting their effectiveness in such contexts. While preprocessing (e.g., imputation) helps, it can cause information loss. This study proposes the RTD (Recovery Tourism Demand Forecast with Imputation) framework, which reconstructs disrupted series using pre-shock trends, then estimates recovery to adjust forecasts. Combining deep learning and time series decomposition, RTD minimizes data loss and improves accuracy. Results show RTD outperforms conventional models, aiding recovery-focused tourism planning. • Define the data misalignment in tourism demand data. • Providing the solution RTD for the tourism demand forecasting under disruption. • Case studies across 20 destinations with outperforming performance.