Trend Analysis of Time Series Data Using Data Mining Techniques

Time series is one of the popular data types that can be found in many domains such as business, medical, meteorological fields, etc. Identifying potential trends in time series is important because it imparts knowledge about what has taken place in the past and what will take place in time to come. Trend analysis in the time series is the practice of collecting and attempting to spot patterns. Various data mining techniques such as clustering, classification, regression, etc. can be used to expose those trends. In this work, we developed a framework to analyze the time series data, which cluster time series according to their similarity. We also introduced a merging algorithm to represent each cluster using a representative series. Trends are detected in a series using Modified Mann-Kendall test.