Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Topic evolution and social interactions: how authors effect research
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Learning information diffusion process on the web
Proceedings of the 16th international conference on World Wide Web
Automatic online news issue construction in web environment
Proceedings of the 17th international conference on World Wide Web
Automatic online news topic ranking using media focus and user attention based on aging theory
Proceedings of the 17th ACM conference on Information and knowledge management
Studying the history of ideas using topic models
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Detecting topic evolution in scientific literature: how can citations help?
Proceedings of the 18th ACM conference on Information and knowledge management
Application of Composite Grey BP Neural Network Forecasting Model to Motor Vehicle Fatality Risk
ICCMS '10 Proceedings of the 2010 Second International Conference on Computer Modeling and Simulation - Volume 02
The web of topics: discovering the topology of topic evolution in a corpus
Proceedings of the 20th international conference on World wide web
Modeling the evolution of topics in source code histories
Proceedings of the 8th Working Conference on Mining Software Repositories
Computers and Electronics in Agriculture
A grey approach for forecasting in a supply chain during intermittentdisruptions
Engineering Applications of Artificial Intelligence
Computers and Industrial Engineering
| Hi-index | 0.00 |
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.