Information Decay in Building Predictive Models Using Temporal Data

Abstract

Most predictive data mining methods are based on the assumption that the historical data involved in building and verifying a model are the best estimator of what will happen in the future. However, the relevance of past to the future depends on the application domain in a specific timeframe. In this paper, we present a method of predicting the future from temporal data using information decay technique that estimate the relevance of the past to the future with the help of the knowledge of the information lifetime of the temporal data. We show how to use a decision tree data mining technique to build an information-decay-based predictive model by introducing an information decay method in impurity measure functions. The relevance of the concepts is proven by comparing two decision tree-based classifiers: one built using information decay over a specific timeframe and the other built setting aside the time factor.