A Dictionary-Based with Stacked Ensemble Learning to Time Series Classification

Dictionary-based methods are one of the strategies that have grown in the realm of time series classification. Particularly, these methods are effective for time series data that have different lengths. Our contribution involves introducing the integration of a dictionary-based technique with stacked ensemble learning. This study is unique since it combines the symbolic aggregate approximation (SAX) with stacking gated recurrent units (GRU) and a convolutional neural network (CNN), referred to as SGCNN, which has not been previously investigated in time series classification. Our approach uses the SAX technique to transform unprocessed numerical data into a symbolic representation. Next, the classification process is done using the SGCNN classifier. Empirical experiments demonstrate that our approach performs admirably across various datasets. In particular, our method achieves the second position among current advanced dictionary-based methods.