Predicting Patent Data using Wavelet Regression and Bayesian Machine Learning

Abstract: Patents are a fundamental part of scientific and engineering work, ensuringprotection of inventions owned by individuals or organizations. Patents areusually made public 18 months after being filed to a patent office, whichmeans that current publicly available patent data only provides informationabout the past. Regression models applied on discrete time series can be usedas a prediction tool to counteract this, building a 18 month long bridge intothe future and beyond. While linear models are popular for their simplicity,Bayesian networks have statistical properties that can produce high forecastingquality. Improvements is also made by using signal processing as patentdata is naturally stochastic. This thesis implements wavelet-based signalprocessing and P CA to increase stability and reduce overfitting. A multiplelinear regression model and a Bayesian network model is then designed andapplied to the transformed data. When evaluated on each data set, the Bayesianmodel both performs better and exhibits greater stability and consistency inits predictions. As expected, the linear model is both smaller and faster toevaluate and train. Despite an increase in complexity and slower evaluationtimes, the Bayesian model is conclusively superior to the linear model. Futurework should focus on the signal processing method and additional layers inthe Bayesian network.