A Comparison of Time Series Model Forecasting Methods on Patent Groups

摘要

The ability to create forecasts and discover trends is a value to almost any industry. The challenge comes in finding the right data and the appropriate tools to analyze and model such data. This paper aims to demonstrate that it may be possible to create technology forecasting models through the use of patent groups. The focus will be on applying time series modeling techniques to a collection of USPTO patents from 1996 to 2013. The techniques used are Holt-Winters Exponential Smoothing and ARIMA. Cross validation methods were used to determine the best fitting models and ultimately whether or not patent data could be modeled as a time series.