Users use the network more and more frequently, and more and more data is published on the network. Therefore, how to find, organize, and use the useful information behind these massive data through effective means, and analyze user intentions is a huge challenge. There are many time series problems in user intentions. Time series have complex characteristics such as randomness and multi-scale variability. Effectively identifying the inherent laws and objective phenomena contained in time series is the purpose of analyzing and processing time series data. Fractal theory provides a new way to analyze time series, and obtains the characteristics and rules of time series from a new perspective. Therefore, this paper introduces the fractal theory to analyze the time series problem, and proposes an improved G-P algorithm to realize the prediction and mining of user intentions. First, the method of array storage instead of repeated calculations is used to improve the method of saturated correlation dimension. Second, the Hurst exponent of the time series is obtained by the variable scale range analysis method. Finally, a fractal model for predicting user intent in short time series is established using the accumulation and transformation method. The experimental results show that the use of fractal theory can effectively describe the relevant characteristics of time series, the development trend of user intentions can be mined from big data, and the prediction model for short time series can be established to achieve information mining of user intentions.
Discussion on the paper “Analyzing short time series data from periodically fluctuating rodent populations by threshold models: A nearest block bootstrap approach”
