Optimization of prediction methods for patents and trademarks in Spain through the use of exogenous variables

Abstract

An accurate forecast of patent and trademark application filings is strategic for resource planning at the Spanish Patents and Trademarks Office and other patent offices, national and supranational. The need for reliable forecasts of patents and trademarks application filings has been accentuated by the current situation of budgeting rationalization imposed by the economic crisis. In this study we have evaluated the suitability and effectiveness of different methodologies for advanced data analysis to predict the number of national patent and trademark applications in the short and medium terms (2011–2014), including the use of exogenous variables or predictors which help to understand the changes in these variables. The inclusion of exogenous variables which explain the behavior of patent and trademark application filings, in particular the investment in R&D and GDP, and the use of advanced predictive analysis techniques, amongst which the most notable are Polynomial Distributed Lags and Intelligent Transfer Function models, have all achieved an improvement upon the prediction and modeling power possessed by the models formerly used to predict trademark and patent series based only on the analysis of time series.