Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

Gunter, Ulrich; Önder, Irem; Smeral, Egon

doi:10.3390/forecast2030012

Cited by 11 publications

(7 citation statements)

References 43 publications

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“…288-294), [92] (pp. [91][92][93][94][95][96][97][98][99][100][101]. The key importance of tourist values is supported by the fact that tourists arrive at a given place because they are interested in the tourist values existing there.…”

Section: Discussionmentioning

confidence: 99%

“…Tourism development forecasts can be found in Álvarez-Díaz Gunter et al [98] (pp. [90][91][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106] and Gunter et al [99] (pp. 211-229).…”

Section: Discussionmentioning

confidence: 99%

“…Depending on the index value, the communes in question were classified into five groups: no tourist traffic (Ws = 0), low tourist traffic intensity (0 < Ws < 100), moderate tourist traffic intensity (100 ≤ Ws < 166), average tourist traffic intensity (166 ≤ Ws < 500), and high tourist traffic intensity (Ws ≥ 500) [68] (pp. [91][92][93][94][95][96][97][98][99][100][101][102][103]. It is impossible to quantify the actual volume of tourist traffic precisely.…”

Section: Methodsmentioning

confidence: 99%

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Indicators of the Tourist Attractiveness of Urban–Rural Communes and Sustainability of Peripheral Areas

Świdyńska

Witkowska-Dąbrowska

2021

Sustainability

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The elements which determine a peripheral area’s level of tourist attractiveness, such as tourist infrastructure and tourist values, should be developed in urban–rural communes in peripheral areas, where tourism may be one of the forces capable of stimulating sustainable development. This study covered urban–rural communes of the province of Warmia and Mazury in Poland. Urban–rural communes are specific areas where urban–rural linkages are often important. The research was carried out in accordance with Hellwig’s taxonomic development pattern method. The study found no complementary relationship between tourism values and tourism infrastructure with regards to creating tourism attractiveness. Tourism attractiveness was found to be more affected by tourism infrastructure. However, in units with larger urban centers, tourist values were found to significantly contribute to tourist attractiveness. The presented results provide a good basis for further research on the impact of global trends on regional development. At the same time, the analyzed framework provides guidance for ensuring the development of local tourism, and the study’s suggested priorities and measures could lead to the development of tourism in peripheral regions, which should in turn attract new investments, create new jobs, and thus develop the economy and the welfare of the population.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Indicators of the Tourist Attractiveness of Urban–Rural Communes and Sustainability of Peripheral Areas

Świdyńska

Witkowska-Dąbrowska

2021

Sustainability

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“…In the second step, the accuracy of forecasts was assessed by means of ex post forecast errors used in the relevant literature on forecasting [21][22][23][24]26,[43][44][45][46][47][48][49]]:…”

Section: Purpose and Methodology Of Researchmentioning

confidence: 99%

Application of Forecasting as an Element of Effective Management in the Field of Improving Occupational Health and Safety in the Steel Industry in Poland

Małysa

2022

Sustainability

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(1) Background: Every day, human beings fall victim to accidents. We implement solutions aimed at reducing accidents in everyday life, but we are not able to eliminate all accidents from our life. This article addresses the issue of forecasting accidents at work in the steel industry in Poland. Particular attention is paid to other accidents, given that those events are most often recorded in the sector under analysis. (2) Methods: The process of predicting quantitative data on the number of persons injured in other accidents in 2009–2018 employed Holt’s models: with an additive and multiplicative trend, with the trend smoothing effect in the multiplicative and additive formula. (3) Results: The forecasts prepared on the basis of Holt’s models and the combined model show a decreasing trend in the number of persons injured in other accidents in the steel sector, which is a positive development in the area of occupational safety and health. (4) Conclusions: The number of persons injured in other accidents at work in the steel sector shows a downward trend, which is significant and valid information for managers. The analysis of the results indicated that the combined forecast model best reflects the accidents at work in the steel industry.

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“…In the field of machine learning, stochastic gradient descent is a widely used weight optimization method, especially in DL. However, there are some problems with the random gradient descent method in practical applications [11][12]. Firstly, the random gradient learning descent method needs to choose a suitable learning rate; If the learning rate is too low, the update will be too slow and time-consuming; If the learning rate is too large, it may lead to failure of convergence, so it is difficult to choose an appropriate learning rate.…”

Section: Training Of DL Prediction Model Networkmentioning

confidence: 99%

Analysis and Prediction of Tourism Data with Deep Learning

2020

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In recent years, deep learning (DL) technology has developed rapidly, which has been highly concerned by academia and industry. It has been widely used in image, voice, natural language processing, big data feature extraction and other aspects, and has also made very significant achievements. It is now a very important research direction in the field of artificial intelligence. The research on data analysis and prediction based on DL has also received more and more attention in recent years and has gradually become a research hotspot. Based on this, this fusion DL technology has studied the analysis and prediction of tourism data (TD). The training of DL prediction model network and TD are briefly analyzed; taking the TD of M province as the research object, this paper analyzes the number of tourists and hotels in the province and the experimental results verify the feasibility and effectiveness of applying the depth learning algorithm to the analysis and prediction of TD.

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Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

Cited by 11 publications

References 43 publications

Indicators of the Tourist Attractiveness of Urban–Rural Communes and Sustainability of Peripheral Areas

Indicators of the Tourist Attractiveness of Urban–Rural Communes and Sustainability of Peripheral Areas

Application of Forecasting as an Element of Effective Management in the Field of Improving Occupational Health and Safety in the Steel Industry in Poland

Analysis and Prediction of Tourism Data with Deep Learning

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