Extracting temporal knowledge from time series: A case study in ecological data
Reggio N. Hartono, Russel Pears, Nikola Kirilov Kasabov, Susan P. Worner · 2014
This research presents a generic framework and methods for mining temporal rules from multiple time-series data and its application to ecological data. The aphids dataset that tracks the trajectory of aphid infestations over time has been well researched in a number of studies. Those studies concentrated on predicting the scale of infestation over time. The focus of our research is to identify environmental factors that predict, in a temporal fashion, high incidence of aphid activity. This required the development of a novel framework for knowledge extraction from multiple time-series data and a method for discretization of numeric data as well-known methods such as SAX did not perform adequately due to the non-Gaussian nature of the data involved. Our experimentation yielded new insights into the environmental factors that may influence pest outbreak which are captured in the form of simple actionable rules that would be of interest to the farming community.