Grey System Theory based prediction for topic trend on Internet

Abstract

Techniques extracting topics from dynamic Internet are relatively matured. However, people cannot accurately predict topic trend so far. Unfortunately, for prediction of topic trend, the availability of data is always very limited owing to the short life circle of topics, especially in such a highly efficient and fast-paced era. Based on Grey Verhulst Model, the paper presents an algorithm to predict topics trend. The principle of Grey Model for prediction application is analyzed and Grey Verhulst Model is established. In the meanwhile, real-world data from Youku (the largest video site in China and something like YouTube) is applied to test our presented algorithm. The average relative error of Grey Verhulst Model is less than 3%. The results show that Grey Verhulst Model has a higher prediction precision. The main contributions of this paper are as follows. First, we introduce Grey System Theory (GST) originated from system theory to the prediction of topics trend and to some extent, solve the problem with a high accuracy; second, to the best of our knowledge, it is the first attempt to employ GST in the field of topic trend prediction.