Google Trends is Misleading You: How to Do Machine Learning with Google Trends Data

Google Trends data presents a significant challenge for machine learning because its values are normalized, with the peak in any selected time window always scaled to 100. This makes it impossible to directly compare data across different time periods, a necessity for time series modeling. A proposed solution is to download data in rolling, overlapping windows, such as 90-day periods with a one-month overlap. This shared period acts as a stable anchor to rescale subsequent data chunks, creating a consistent long-term dataset while mitigating randomness from sampling and rounding errors.