Forecasting Automobile Sales in Turkey with Artificial Neural Networks

Kaya, Aycan; Kaya, Gizem; Çebi, Ferhan

doi:10.4018/ijban.2019100104

Cited by 16 publications

(12 citation statements)

References 17 publications

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“…At the beginning of economic growth, most countries have a higher cost associated with the interest rate. The research results are in line with Nawi et al (2013); Kaya (2019); Lee and Kang (2015); Pant and Nidugala (2016).…”

Section: Resultssupporting

confidence: 91%

“…The results do not provide a clear explanation of the economic risks in the long run. Kaya, Kaya and Cebi (2019) focus on factors that affect automobile sales and estimate automobile sales in Turkey by using an artificial neural network (ANN), Auto Regressive Integrated Moving Average (ARIMA), and time-series decomposition techniques. The dollar exchange rate, households' financial status, the industrial production index and the logarithmic form of automobile sales before one month are found to significantly affect automobile sales.…”

Section: Literature Reviewmentioning

confidence: 99%

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Macroeconomic Determinants of Auto Sales in ASEAN: An Empirical Study in Five Major ASEAN Countries

Johan

2020

J. ASEAN Studies

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This research examines the determinants of car sales in ASEAN countries. The research concentrates on five macroeconomic variables (consumer price index, gross domestic product (GDP) per capita, changes in gross domestic product per capita, foreign exchange rate, and interest rate). The total sample is 12 years of automobile sales in five ASEAN countries from 2005 – 2016. The five ASEAN countries are Indonesia, Thailand, Malaysia, Singapore, and Vietnam. This paper used the multilinear regression method with Statistical Package for the Social Sciences (SPSS) software to test the research model. For interest-rate variables, we used a lag of one year. The empirical results show that the previous period for inflation, gross domestic product per capita, interest rate, and the foreign exchange rate significantly influenced on car sales in five ASEAN countries. The growth of GDP per capita does not influence car sales.

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Section: Resultssupporting

confidence: 91%

Section: Literature Reviewmentioning

confidence: 99%

Macroeconomic Determinants of Auto Sales in ASEAN: An Empirical Study in Five Major ASEAN Countries

Johan

2020

J. ASEAN Studies

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“…The rise and fall of the price can directly affect the purchase intention. Excessively high prices can restrict consumers' purchase intention [38]. In recent years, the downward trend of automobile prices has also greatly promoted the purchase rate of automobiles [39].…”

Section: Determination Of Influencing Factorsmentioning

confidence: 99%

“…Based on the above analysis of influence factors, this section takes the consumer price index [36,37,40] (X 1 ), money supply [37] (X 2 ), automobile price [38,39] (X 3 ), steel output [40] (X 4 ), rubber tire output [41] (X 5 ) and diesel output [41] (X 6 ) as the external factors affecting automobile sales, combined with consumer star rating data of power [38] (X 7 ) and comfort [38,41] (X 8 ) as the characteristic factors of a multi-factor automobile sale prediction model.…”

Section: Determination Of Influencing Factorsmentioning

confidence: 99%

Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm

Wang

Hou

et al. 2022

Mathematics

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With the development of the Internet and big data, more and more consumer behavior data are used in different forecasting problems, which greatly improve the performance of prediction. As the main travel tool, the sales of automobiles will change with the variations of the market and the external environment. Accurate prediction of automobile sales can not only help the dealers adjust their marketing plans dynamically but can also help the economy and the transportation sector make policy decisions. The automobile is a product with high value and high involvement, and its purchase decision can be affected by its own attributes, economy, policy and other factors. Furthermore, the sample data have the characteristics of various sources, great complexity and large volatility. Therefore, this paper uses the Support Vector Regression (SVR) model, which has global optimization, a simple structure, and strong generalization abilities and is suitable for multi-dimensional, small sample data to predict the monthly sales of automobiles. In addition, the parameters are optimized by the Grey Wolf Optimizer (GWO) algorithm to improve the prediction accuracy. First, the grey correlation analysis method is used to analyze and determine the factors that affect automobile sales. Second, it is used to build the GWO-SVR automobile sales prediction model. Third, the experimental analysis is carried out by using the data from Suteng and Kaluola in the Chinese car segment, and the proposed model is compared with the other four commonly used methods. The results show that the GWO-SVR model has the best performance of mean absolute percentage error (MAPE) and root mean square error (RMSE). Finally, some management implications are put forward for reference.

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“…automobile sharing [12], etc. There are various research that focus on the automobile sales forecast and the application of new measurement methods and the improvement of measurement accuracy, such as time series methods [13], structural relationship recognition methods [14], analysis and prediction based on web crawler data [15] and big data [16], prediction based on artificial intelligence algorithms [17] [18].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Evolution Characteristics and Influencing Factors of China’s Automobile Market since Joining the WTO

Wu¹,

Lyu²,

Lai³

2022

JGIS

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Since joining the WTO, China's automobile market has shown a rapid development trend, and the automobile market is becoming more and more important to China's economic recovery and high-quality development. The automobile manufacturing industry is one of the pillar industries of China, but facing downward pressure since 2018. The paper studies spatiotemporal evolution characteristics and influencing factors of automobile market since WTO Accession using methods including ESDA, DTW cluster analysis and Spatial panel Dubin model. The result shows that: 1) China's automobile sales have grown rapidly and three development stages have occurred since WTO Accession; 2) Four types of China's automobile markets have significant spatial differentiation, while the same pattern present spatial agglomeration characteristics; 3) The crucial reasons for spatial separation of production and sales in China's automobile market include implementation of purchase restrictions in more and more cities, gradual consolidation of spatial pattern of automobile production, and the fact that some automobile production areas are far away from consumer market; 4) The provincial spatial weighted average centers of automobile sales are mainly distributed in southeast Henan, and show a trend of moving to the southwest; 5) The estimated coefficients of factors such as GDP, financial added value, the proportion of highway, the volume of highway freight, and implementation of automobile consumption incentive policies are all significantly positive, and some factors have positive spatial spillover effects. Existing research on the automobile market lacks analysis based on long-time series data. This study uses long-time series data to provide a certain reference for future research in related directions.

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Forecasting Automobile Sales in Turkey with Artificial Neural Networks

Cited by 16 publications

References 17 publications

Macroeconomic Determinants of Auto Sales in ASEAN: An Empirical Study in Five Major ASEAN Countries

Macroeconomic Determinants of Auto Sales in ASEAN: An Empirical Study in Five Major ASEAN Countries

Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm

Spatiotemporal Evolution Characteristics and Influencing Factors of China’s Automobile Market since Joining the WTO

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