The Proper Use of Google Trends in Forecasting Models
This addresses a critical issue for forecasters in academia and industry who rely on Google Trends, but it is incremental as it builds on existing knowledge about data variability.
The paper tackles the problem of inconsistent Google Trends data samples leading to arbitrary conclusions in forecasting models, and proposes methods to overcome this obstacle.
It is widely known that Google Trends have become one of the most popular free tools used by forecasters both in academics and in the private and public sectors. There are many papers, from several different fields, concluding that Google Trends improve forecasts' accuracy. However, what seems to be widely unknown, is that each sample of Google search data is different from the other, even if you set the same search term, data and location. This means that it is possible to find arbitrary conclusions merely by chance. This paper aims to show why and when it can become a problem and how to overcome this obstacle.