Network Forum Posts Sequence Of Self-similarity Modeling And Forecasting

With the introduction of the concept Web2.0, high-speed Internet gets more and more attention. Online forums, blogs and other forms of communication have become hot Internet applications. On the one hand, the topic is a major focus of web forum research; the other hand, due to various business decisions (such as product market research or advertising), the forum or blog user participation is becoming a new hotspot, the number of articles on the forum has also recently received attention. Accurate measurement of dynamic changes is essential for analysis, understanding and simulation in online forums, and also has a fundamental significance to guide the design of control scheme.In this paper, the number of articles in Online Forum is to be studied. This paper describes several models. And on that basis, this paper raised the method for forecasting the sequence of the number of articles. First, through analysis of actual data, this paper validates and certificates the self-similarity of article number sequence. Then, the short and long correlation time series analysis methods are applied to the modeling and analysis of self-similarity. This paper gives the modeling method and procedures of Markov models, FARIMA model. In order to avoid the complexity of structure identification and parameter estimation on the FARIMA model,λ-ARMA model is proposed based on the traditional ARMA model. Experiments on real data verify the applicability of FARIMA model and X-ARMA model.An important use of modeling is to predict, this paper gives the prediction algorithm and steps. Through experimental verification of different models, this paper validates the different applicability within the confidence interval.The study results show that from the perspective of the number of articles are necessary and feasible. This work will provide references for the next generation of networks, especially in online forums, blog.