New Technique Based on Time Series Variation Using Field Association Terms

This paper studies the effects of the time change using new technique based on frequencies of specific terms connected to the field called Field Association (FA) terms in a given period of time. The paper also suggests a new method for evaluating automatically the stabilization classes of FA terms to point out the popularity of list of FA terms depending on time change and to improve the precision of Decision Tree (DT). Moreover, the new approach manipulates the problem of the scattering data number between classes to improve the performance of DT precision. The presented method evaluated by emulation the result of 4.15 MB (1,245 files) and 14 super F-measure is %90.8 for Increment class, %99.5 for Steady class and %68.1 for Decrement class using random sampling method with FA words. However, F-measure is %93.6 for Increment class, %99.8 for Steady class and %75.7 for Decrement class using data copying method with FA words.