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Predicting Japanese Car Sales with Google Trends and Machine Learning
Lo, Kuang Ta
Yang, Tzu Ting
|Issue Date: ||2018-07-12 17:20:15 (UTC+8)|
|Abstract: ||Computers and the Internet has been significantly changing our lives over the past few decades and bringing both a lot of opportunities and challenges to our lives. Internet, on the 1 hand, possess a lot of free and important information. For example, information about consumers’ moods and preferences that can be extracted from the Web using Google Trends search index data which is undoubtedly precious for market research and forecast. While computers and their computation abilities using machine learning make it feasible to improve to improve task performance, particularly forecasting and planning.
The aim of this research is to utilize both tools – Google Trends data and Least Absolute Shrinkage and Selection Operator (LASSO, a machine learning method) in forecasting Japanese car sales. This paper pursues two main goals: to compare the machine learning method performance with conventional and human-created models and to identify if Google Trend data helps to improve forecasting model for Japanese car sales.
From the results of this research it can be concluded that machine learning methods definitely have some positive implications for forecasting. LASSO definitely outperform human-judgment. Generally, LASSO models with optimal penalty size are very comparable in their out of sample prediction accuracy to autoregressive models. LASSO with optimal lambda also creates models that include a limited number which is undoubtedly easier to interpret.
Google Trends data should be treated with care. It is, in generally, advised to run LASSO-regression when working with Google data as LASSO is able to identify the right lags for the Google search indexes that is of a critical importance due to the fact that different brands might have different characteristics and different consumers.
|Reference: ||Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.
Bortoli, C., & Combes, S. Contribution from Google Trends for forecasting the short-term economic outlook in France: limited avenues.
Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88 (s1), 2-9.
Diebold, F.X. (2017). Forecasting. Pennsylvania: Department of Economics, University of Pennsylvania. Retrieved from: http://www.ssc.upenn.edu/~fdiebold/Textbooks.html
Fantazzini, D., & Toktamysova, Z. (2015). Forecasting German car sales using Google data and multivariate models. International Journal of Production Economics, 170, 97-135.
Gevelber, L. (2016, March). The Car-Buying Process: One Consumer's 900+ Digital Interactions. Retrieved from https://www.thinkwithgoogle.com/consumer-insights/consumer-car-buying-process-reveals-auto-marketing-opportunities/
Google Inc. (2018). How Trends data is adjusted. Retrieved from https://support.google.com/trends/answer/4365533?hl=en&ref_topic=6248052
Hand, C., & Judge, G. (2012). Searching for the picture: forecasting UK cinema admissions using Google Trends data. Applied Economics Letters, 19(11), 1051-1055.
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International journal of forecasting, 22(4), 679-688.
Kotler, P., & Keller, K. L. (2012). Marketing Management. Global Edition 14e, London: Pearson Education Limited 2012
Li, J., & Chen, W. (2014). Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models. International Journal of Forecasting, 30(4), 996-1015.
MAE and RMSE — Which Metric is Better? (2016, March 23). Retrieved from https://medium.com/human-in-a-machine-world/mae-and-rmse-which-metric-is-better-e60ac3bde13d
Muehlen, M. (2017) Improved Sales Forecasting with Consumer Behavior. IMES, National Chengchi University
Sagaert, Y. R., Aghezzaf, E. H., Kourentzes, N., & Desmet, B. (2017). Temporal big data for tire industry tactical sales forecasting. Interfaces.
Shi, Y., Liu, X., Kok, S. Y., Rajarethinam, J., Liang, S., Yap, G., ... & Lo, A. (2016). Three-month real-time dengue forecast models: an early warning system for outbreak alerts and policy decision support in Singapore. Environmental health perspectives, 124(9), 1369.
Small, G., & Wong, R. (2002). The validity of forecasting. In A Paper for Presentation at the Pacific Rim Real Estate Society International Conference, Christchurch, New Zealand (pp. 1-14).
Spiegelm B. (2015, February 10). The Google Trends Data Goldmine. Retrieved from https://marketingland.com/google-trend-goldmine-117626
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 267-288.
Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79-82.
Yang T.T. (2018). Machine Learning and Casual Inference [PowerPoint slides]. Retrieved from: https://drive.google.com/file/d/1wUfA6RzcwHkOTId7_dA86-PJ67T6-xgI/view
|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G0105266011|
|Data Type: ||thesis|
|Appears in Collections:||[應用經濟與社會發展英語碩士學位學程 (IMES) ] 學位論文|
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