Modelling and Forecasting Zimbabweâ€™s Tourist Arrivals Using Time Series Method: A Case Study of Victoria Falls Rainforest

Makoni, Tendai; Chikobvu, Delson

doi:10.25159/1998-8125/3791

Cited by 12 publications

(6 citation statements)

References 45 publications

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“…Dimana tujuan utamanya adalah selain bekerja dan lamanya kunjungan kurang dari satu tahun (BPS, 2018) (Kumar & Sharma, 2016). Hasil penelitian lainnya tentang peramalan kedatangan wisatawan di Zimbabwe juga menunjukkan bahwa metode SARIMA merupakan model yang baik dibanding model lainnya (Makoni & Chikobvu, 2018). (ARIMA) yang memiliki pola musiman (Box et al, 2016).…”

Section: Literatur Reviewunclassified

Prediksi Jumlah Kunjungan Wisatawan Mancanegara ke Indonesia

Meimela

2021

MWS

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Predict the number of foreign tourist's visit Indonesia. In 2020 the Ministry of Tourism and Creative Economy has targeted the number of foreign tourists visiting Indonesia as many as 17 million visits. However, the number of foreign tourist visits decreased cumulatively (January- July 2020) by 64.64 per cent compared to the same period in 2019. Based on these conditions, it is significant to make accurate predictions to see if the target will be achieved or not in the future. One of the prediction methods used is Seasonal ARIMA (Autoregressive Integrated Moving Average). This model predicted the predictable number of foreign tourists visit in 2020 forecast to pass the target

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Section: Literatur Reviewunclassified

Prediksi Jumlah Kunjungan Wisatawan Mancanegara ke Indonesia

Meimela

2021

MWS

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“…The current paper employ the hierarchical forecasting technique, Quantile Regression Averaging (QRA) and PIs which capture tourism dynamics and quantify the uncertainities in future values due to parameter estimation unlike the hierarchical forecasting model developed by [29] which only captures tourism seasonality. QRA PIs provide supplementary information useful for effective marketing strategies and decision-making ( [30]; [8]).…”

Section: Literature Reviewmentioning

confidence: 99%

Hierarchical Forecasting of the Zimbabwe International Tourist Arrivals

Makoni

Chikobvu

Sigauke

2021

Stat., optim. inf. comput.

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The objectives of the paper is to: (1) adopt the hierarchical forecasting methods in modelling and forecasting international tourist arrivals in Zimbabwe; and (2) coming up with Zimbabwe international tourist arrivals Prediction Intervals (PIs) in Quantile Regression Averaging (QRA) to hierarchical tourism forecasts. Zimbabwe’s monthly international tourist arrivals data from January 2002 to December 2018 was used. The dataset used was before the COVID-19 period and were disaggregated according to the purpose of the visit (POV). Three hierarchical forecasting approaches, namely top-down, bottom-up and optimal combination approaches were applied to the data. The results showed the superiority of the bottom-up approach over both the top-down and optimal combination approaches. Forecasts indicate a general increase in aggregate series. The combined methods provide a new insight into modelling tourist arrivals. The approach is useful to the government, tourism stakeholders, and investors among others, for decision-making, resource mobilisation and allocation. The Zimbabwe Tourism Authority (ZTA) could adopt the forecasting techniques to produce informative and precise tourism forecasts. The data set used is before the COVID-19 pandemic and the models indicate what could happen outside the pandemic. During the pandemic the country was under lockdown with no tourist arrivals to report on. The models are useful for planning purposes beyond the COVID-19 pandemic.

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“…The simulation study shows that the method performs well compared to the top-down approach and the bottom-up method, the proposed method demonstrates by forecasting Australian tourism demand where the data are disaggregated by the purpose of travel and geographical region. In [ 3 ], Makoni and Chikobvu presented a paper that aims to model and forecast the Victoria Falls Rainforest tourism demand using hierarchical forecasting methods, the top-down, bottom-up, and optimal combination approaches are adopted. The exponential smoothing techniques (EST) and the autoregressive integrated moving average (ARIMA) methods are the forecasting methods considered.…”

Section: Introductionmentioning

confidence: 99%

Enhancing forecast accuracy using combination methods for the hierarchical time series approach

Mohamed

2023

PLoS ONE

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This study aims to investigate whether combining forecasts generated from different models can improve forecast accuracy rather than individual models using the hierarchical time series. Various approaches of hierarchical forecasting have been considered; a bottom-up, top-down, and an optimal combination approach. Autoregressive moving averages (ARIMA) and exponential smoothing (ETS) were used as forecasting models in creating forecasting for all levels in the hierarchy to show the effect of different forecasting methods for each hierarchical model. The results indicated that the Minimum Trace Sample estimator (MinT-Sample) and the bottom-up approaches with the ARIMA model have good predictive performance than other approaches. Moreover, the forecasts from the MinT-Sample and bottom-up approaches were combined using five different combining methods. The experimental results showed that the (AC) method is superior to all other combining methods and more accurate than other individual models at level zero (international total trade in Egypt) and level one (total exports, and total imports). So, combining forecasts generated from different models by hierarchical time series leads to more accurate forecasting of the value of imports and exports which will improve the overall international trade performance, and that is through using the forecasting values of imports and exports to plan for improving the trade balance and drawing up a more efficient production policy. Finally, the study recommends using hierarchical forecasting methods in the areas of international trade, and the Ministry of Commerce and Industry could adopt the results of this study to produce precise forecasts for international trade. Moreover, the results of this study are to be a guide for the researchers to apply these approaches in other fields to improve the performance of forecasting.

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Modelling and Forecasting Zimbabweâ€™s Tourist Arrivals Using Time Series Method: A Case Study of Victoria Falls Rainforest

Cited by 12 publications

References 45 publications

Prediksi Jumlah Kunjungan Wisatawan Mancanegara ke Indonesia

Prediksi Jumlah Kunjungan Wisatawan Mancanegara ke Indonesia

Hierarchical Forecasting of the Zimbabwe International Tourist Arrivals

Enhancing forecast accuracy using combination methods for the hierarchical time series approach

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