UCLA

AMP is a web page format that Google has developed for optimizing search results and mobile display. In this thesis, we will analyze the daily data for revenues, pageviews, and RPMs of a particular website that uses both AMP and regular web pages from 2016 to 2018. After data cleaning and transformation, we will utilize spectral analysis, regressions, multiplicative decomposition using LOESS, ARIMA modeling and other methods to analyze the time-series data, separate the seasonality and trend components, and build two different models and generate predictions. We will also analyze the specific impact of suspending AMP-enabled pages on the advertisement revenue of a website, and provide casual inference on the behavior of total revenue immediately after suspending AMP-enabled pages.