Forecasting German car sales using Google data and multivariate models

Fantazzini, Dean; Toktamysova, Zhamal

doi:10.1016/j.ijpe.2015.09.010

Cited by 81 publications

(52 citation statements)

References 57 publications

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“…Goel et al (2010) examine, among other things, the relationship between the use of search engines and real estate sales, as well as disease prevalence. Other researchers have tested whether the Google Trends Automotive Index can improve predictions of car sales in Chile (Carriere-Swallow and Labbe, 2013) and in Germany (Fantazzini and Toktamysova, 2015), have developed forecasts of the real oil price using Google search results (Fantazzini and Fomichev, 2014), have stressed that Google Flu Trends data can follow the path of an outbreak using United States data from 2003 to 2009 (Dukic et al, 2012). Dergiades et al (2018) proposed corrections in terms of language bias and the platform bias of search engines to improve the predictive power of forecasting.…”

Section: Introductionmentioning

confidence: 99%

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Havránek

Zeynalov

2019

Tourism Economics

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In this article, we examine the usefulness of Google Trends data in predicting monthly tourist arrivals and overnight stays in Prague during the period between January 2010 and December 2016. We offer two contributions. First, we analyze whether Google Trends provides significant forecasting improvements over models without search data. Second, we assess whether a high-frequency variable (weekly Google Trends) is more useful for accurate forecasting than a low-frequency variable (monthly tourist arrivals) using mixed-data sampling (MIDAS). Our results suggest the potential of Google Trends to offer more accurate predictions in the context of tourism: we find that Google Trends information, both 2 months and 1 week ahead of arrivals, is useful for predicting the actual number of tourist arrivals. The MIDAS forecasting model employing weekly Google Trends data outperforms models using monthly Google Trends data and models without Google Trends data.

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Section: Introductionmentioning

confidence: 99%

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Havránek

Zeynalov

2019

Tourism Economics

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“…Fantazzini and Toktamysova (2015) investigate the out-ofsample accuracy of multivariate models using Google search data and economic variables to predict monthly sales of several car brands in Germany [3]. They find that Google data-based prediction models outperform competing models especially for forecast horizons longer than 12 months.…”

Section: A Prediction Of Car Salesmentioning

confidence: 99%

“…Hence, sales forecasts have become an integral component of supply chain processes. Because automotive manufacturers have implemented built-to-forecast vehicle production systems [2], accurate predictions are indispensable to ensure efficient production processes, optimize inventory levels, and improve the overall market performance [3]. Moreover, increasing product individualization places ever-higher demands on business information systems [4] and material requirements planning [5].…”

Section: Introductionmentioning

confidence: 99%

Predicting Automotive Sales using Pre-Purchase Online Search Data

Wachter¹,

Widmer²,

Klein³

2019

Annals of Computer Science and Information Systems

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Sales forecasting is an essential element for implementing sustainable business strategies in the automotive industry. Accurate sales forecasts enhance the competitive edge of car manufacturers in the effort to optimize their production planning processes. We propose a forecasting technique that combines keyword-specific customer online search data with economic variables to predict monthly car sales. To isolate online search data related to pre-purchase information search, we follow a backward induction approach and identify those keywords that are frequently applied by search engine users. In a set of experiments using real-world sales data and Google Trends, we find that our keyword-specific forecasting technique reduces the out-of-sample error by 5% as compared to existing techniques without systematic keyword selection. We also find that our regression models outperform the benchmark model by an out-of-sample prediction accuracy of up to 27%.

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“…In other words, the residuals of each variable are added into the estimation. 16 The VECM can be derived from the auto-regressive distribution hysteresis model, and each equation in the VAR is an auto-regressive distribution hysteresis model. Thus, the VECM can be considered as the VAR with cointegration constraints.…”

Section: The Evaluation Of Forecasting Performancementioning

confidence: 99%

Chinese automobile sales forecasting using economic indicators and typical domestic brand automobile sales data: A method based on econometric model

Gao

Xie

Cui

et al. 2018

Advances in Mechanical Engineering

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Accurate sales forecasting plays an increasingly important role in automobile companies due to fierce market competition. In this article, an econometric model is proposed to analyze the dynamic connections among Chinese automobile sales, typical domestic brand automobile (Chery) sales, and economic variables. Four tests are required before modeling, which include unit root, weak exogeneity, cointegration, and Granger-causality test. The selected economic variables consist of consumer confidence index, steel production, consumer price index, and gasoline price. Monthly is used to empirical analysis and the result shows that there is long-term cointegration relationship between Chinese automobile sales and the endogenous variables. A vector error correction model in econometric based on cointegration is applied to quantify long-term impact of endogenous variables on Chinese automobile sales. Compared with other classical timeseries methods, root mean square error (0.1243) and mean absolute percentage error (10.2015) by vector error correction model for 12-period forecasting are minimal. And the forecasting result is better when the impact of Chery sales is considered. That means that the fluctuation trends of Chinese automobile sales and typical domestic brand automobile sales are closely linked.

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Forecasting German car sales using Google data and multivariate models

Cited by 81 publications

References 57 publications

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

Predicting Automotive Sales using Pre-Purchase Online Search Data

Chinese automobile sales forecasting using economic indicators and typical domestic brand automobile sales data: A method based on econometric model

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