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Combining forecasts has been widely applied as one of the approaches to increase the predictive power over the past few decades. With the promising results, it has seen an explosion in the number of articles on combining forecasts, of which review of the literature become resources taxing and causes researcher's cognitive burden. This study applies a scientometrics analysis to demonstrate the intellectual maps about the networking of research work through co-citation analysis and collaboration analysis. A total of 184 publications in 1965 to 2020 related to combining forecast in financial markets were obtained from WoS database and were analysed. The keyword analysis identified five clusters of research: 1) model performance; 2) time series model error and volatility; 3) time series and tests; 4) forecast evaluation; 5) model efficiency in the combining research area and the evolution of combining forecasts over the study periods were also discussed. The visual mapping results provide an overview of combining forecast development which helps other researchers in formulating their research directions.

Many past studies conducted have explored and focused primarily on forecasting and predicting tourism demand. However, this research aims to concentrate on the aspect of how tourism demand volatility can be forecasted and accounted for, rather than solely on how we can forecast and anticipate tourism demand itself. Seven source countries for monthly tourist arrivals in Malaysia will be considered and focused as a case study in this paper. Furthermore, the model theory of generalised autoregressive conditional heteroscedastic (GARCH) family volatility are explored in its relevance and introduced in this study. Additionally, the performance and evaluation of a new adaptive forecasting method, smooth transition exponential smoothing (STES), will be compared to GARCH-type models and several ad hoc methods for Malaysia's tourism demand volatility forecasting and modelling. Notably, our research conducted reported findings that monthly seasonality impacts existed in the mean equation, and the GARCH family models revealed that news shock continuously impact on Malaysia's tourism demand volatility. The EGARCH and GJR models utilised in this study also exhibited the asymmetric and leverage effects. Interestingly, it was also observed that the STES model surpassed GARCH family models and several ad hoc methods in forecasting and modelling.