Evaluating and Forecasting Room Demand in Tourist Spot using Holt-Winters Method

Rosy, C. Premila; Ponnusamy, R.

doi:10.5120/ijca2017915072

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…Temperature, humidity and light intensity. The summation of the results of each tourist attraction will be compared with the sum results of each tourist attraction with a small summation value as a recommendation for tourist attractions for visitors [14]. The table of weighting results on each waterfall can be seen in the following tables 3, 4, and 5 below:…”

Section: Weighted Prediction Resultsmentioning

confidence: 99%

Implementation of Autoregressive Integrated Moving Average for Tourism Recommendation in Pamijahan Area Based on Internet of Things

2023

IJETER

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Pamijahan is one of the sub-districts located in Bogor regency, West Java. Pamijahan has a variety of tourist destinations, one of which is the Cigamea waterfall, Cikuluwung waterfall, and the Seribu watering problem when visiting tourists, one of them is the weather factor such as rainy weather where Bogor has a highland area so very big influence on the variation of rain there and visitors have difficulty in choosing tourist sites with sunny conditions, the solution to the problem above is to predict the weather during rainy weather in the tourist areas of Cigamea waterfall, Cikuluwung waterfall, and Seribu waterfall using the Autoreggressive Integrated Moving Average method as weather prediction process uses Time Series data with the help of the Internet of Things tool in data retrieval, the weather prediction testing process uses the Autoreggressive Integrated Moving Average method by calculating the value of MSE (Mean Square Error) and MAPE (Mean Absolute Presentage Error) as minimum as possible to see the level of accuracy of the weather prediction process at each designated tourist site. The results of this study indicate that weather forecasting in Cigamea, Cikuluwung and Seribu waterfalls shows the smallest MSE (Mean Square Error) value, the waterfall that has the smallest MSE value, the Seribu waterfall with the Mean Square Error value of the temperature sensor 0.06, Air Humidity 0.59, and Light Intensity 13.30, so that tourist attractions that can be used as tourist attractions for visitors are the Seribu Waterfall.

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Section: Weighted Prediction Resultsmentioning

confidence: 99%

Implementation of Autoregressive Integrated Moving Average for Tourism Recommendation in Pamijahan Area Based on Internet of Things

2023

IJETER

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“…Seasonal time series modeling methods also include the Holt-Winters model [17,18]. This model is rather widely used to model and forecast the tourist flow [19][20][21].…”

Section: Review Of Tourist Flow Modeling Methodsmentioning

confidence: 99%

Tourist Flow Forecasting Approach

Остапенко¹,

Usenko²

2020

Proceedings of the International Scientific Conference "Far East Con" (ISCFEC 2020)

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This article discusses the issues of using methods of analyzing and forecasting seasonal time series to model the tourist flow as one of the most significant parameters of tourism operations. The authors considered reasonability of using the Holt-Winters approach to model time series, estimated model's coefficients, and proposed a forecasting model. The authors also demonstrated use of the Holt-Winters approach to model parameters of tourism operations for a "short series"-the tourist flow to the Republic of Crimea at this stage of the peninsula's development.

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“…Exponential smoothing methods have been developed by Holt (2004) and Winters (1960). The HW approach is an extension of EWMA incorporating linear trend and seasonality into the exponential smoothing (Rosy and Ponnusamy, 2017). Guojun and Ningning (2021) suggested exponential smoothing and ARIMA predict the number of tourists and the total amount of tourism consumption in Guangxi.…”

Section: Time Series Modelsmentioning

confidence: 99%

Improved tourism demand forecasting with CIR# model: a case study of disrupted data patterns in Italy

Bufalo,

Orlando

2023

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Purpose This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature. Design/methodology/approach Typically, tourism demand tends to follow regular trends, such as low and high seasons on a quarterly/monthly level and weekends and holidays on a daily level. The data set consists of nights spent in Italy at tourist accommodation establishments as collected on a monthly basis by Eurostat before and during the COVID-19 pandemic breaking regular patterns. Findings Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. In addition, given the importance of accurate forecasts, many studies have proposed novel hybrid models or used various combinations of methods. Thus, although there are clear benefits in adopting more complex approaches, the risk is that of dealing with unwieldy models. To demonstrate how this approach can be fruitfully extended to tourism, the accuracy of the CIR# is tested by using standard metrics such as root mean squared errors, mean absolute errors, mean absolute percentage error or average relative mean squared error. Research limitations/implications The CIR# model is notably simpler than other models found in literature and does not rely on black box techniques such as those used in neural network (NN) or data science-based models. The carried analysis suggests that the CIR# model outperforms other reference predictions in terms of statistical significance of the error. Practical implications The proposed model stands out for being a viable option to the Holt–Winters (HW) model, particularly when dealing with irregular data. Social implications The proposed model has demonstrated superiority even when compared to other models in the literature, and it can be especially useful for tourism stakeholders when making decisions in the presence of disruptions in data patterns. Originality/value The novelty lies in the fact that the proposed model is a valid alternative to the HW, especially when the data are not regular. In addition, compared to many existing models in the literature, the CIR# model is notably simpler and more transparent, avoiding the “black box” nature of NN and data science-based models.

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Evaluating and Forecasting Room Demand in Tourist Spot using Holt-Winters Method

Cited by 4 publications

References 2 publications

Implementation of Autoregressive Integrated Moving Average for Tourism Recommendation in Pamijahan Area Based on Internet of Things

Implementation of Autoregressive Integrated Moving Average for Tourism Recommendation in Pamijahan Area Based on Internet of Things

Tourist Flow Forecasting Approach

Improved tourism demand forecasting with CIR# model: a case study of disrupted data patterns in Italy

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