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.

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