Addressing Google Trends inconsistencies
研究了谷歌趋势搜索量指数因抽样导致的不一致性，通过建模和模拟数据生成过程，量化了平均提取对序列的平滑效果，并推导出构建一致指数的建议。
Google Trends reports the evolution of the popularity of internet searches. Its main output is the Search Volume Index (SVI), a relative measure of the popularity of a term computed using a sample of the searches. Due to the sampling, the SVI series are not entirely consistent, as the same query produces different results that can widely change from day to day. This paper investigates the nature of these inconsistencies by modeling and simulating the data-generating process. Simulations are applied to describe how a typical time series is distorted due to the sampling process and to quantify how averaging extractions smoothes the series. Finally, a relationship between term popularity, series dispersion, and the averaged extractions is derived, so recommendations for constructing consistent SVIs can be provided.