Quantitative Trendspotting
提出一种结构动态因子分析模型，从大量时间序列中提取关键潜在动态因子，分离季节性波动与非季节性趋势，用于改进定量趋势发现方法，并以汽车品牌搜索数据为例验证。
Trendspotting has become an important marketing intelligence tool for identifying and tracking general tendencies in consumer interest and behavior. Currently, trendspotting is done either qualitatively by trend hunters, who comb through everyday life in search of signs indicating major shifts in consumer needs and wants, or quantitatively by analysts, who monitor individual indicators, such as how many times a keyword has been searched, blogged, or tweeted online. In this study, the authors demonstrate how the latter can be improved by uncovering common trajectories hidden behind the coevolution of a large array of indicators. The authors propose a structural dynamic factor-analytic model that can be applied for simultaneously analyzing tens or even hundreds of time series, distilling them into a few key latent dynamic factors that isolate seasonal cyclic movements from nonseasonal, nonstationary trend lines. The authors demonstrate this novel multivariate approach to quantitative trendspotting in one application involving a promising new source of marketing intelligence—online keyword search data from Google Insights for Search—in which they analyze search volume patterns across 38 major makes of light vehicles over an 81-month period to uncover key common trends in consumer vehicle shopping interest.