The Proper Use of Google Trends in Forecasting Models
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 2 of its 24 references that are in the map. First posted on arXiv 7 Apr 2021.
Builds on
2 of 24 references.
References · 2
- 2020Short-Term Covid-19 Forecast for Latecomerscited by 1
- 2020Lockdown effects in US states: an artificial counterfactual approachcited by 1
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2021Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trendscited by 15
- 2022POP: Mining POtential Performance of new fashion products via webly cross-modal query expansioncited by 2
- 2026Forecasting Commencing Enrolments Under Data Sparsity: A Zero-Shot Time Series Foundation Models Framework for Higher Education Planningcited by 0
- 2026Forecasting Commencing Enrolments Under Data Sparsity: A Zero-Shot Time Series Foundation Models Framework for Higher Education Planning12 Feb 2026
- 2022POP: Mining POtential Performance of new fashion products via webly cross-modal query expansion22 Jul 2022
- 2021Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends20 Sep 2021