Unsupervised Time Series Classification for Climate Data

Abstract

Outstanding success of Convolutional Neural Net- work image classification in the last few years in- fluenced application of this technique to a vari- ety of embeddable entities. CNN image classifica- tion methods are getting high accuracies but they are based on supervised machine learning that re- quires labeling of input data and do not help to understand unknown data. In this study we in- troduce unsupervised machine learning model that categorizes entity pairs to classes of similar and non similar pairs by converting pairs of entities to mirror vectors, transforming mirror vectors to Gramian Angular Fields (GAF) images and clas- sifying images using CNN transfer learning classi- fication. Based on climate data we demonstrated several scenarios that show when this model is re- liable for time series pair classification.