To classify time series data with the traditional way, we need a huge labeled data as the training phase. In reality, the number of labeled data is often smaller and there is a huge number of unlabeled data. At that point, we will manually label those unlabeled data. However, it is time-consuming and expensive. Hence, we do active learning to efficiently querying the data and minimizing the total cost of labeling. Meanwhile, when we observe time series data, we might see a periodic pattern there. As we know that periodicity is one of the general aspects of time series data. By discovering potential periods in time series data, we can get more important information about the data. We might be able to label the data by knowing this information. Here we aim to provide an approach which is able to analyze periodicity in time series data to help active learning to do a better job and achieve higher accuracy using less number of labeled data. Through periodicity analysis, our approach can extract temporal features, select the best unlabeled instances, and label the unlabeled instances. Our approach presents the algorithm to analyze the periodic pattern for handling multi-class time series sequence and provide the state of the art results when combine it with active learning.

To classify time series data with the traditional way, we need a huge labeled data as the training phase. In reality, the number of labeled data is often smaller and there is a huge number of unlabeled data. At that point, we will manually label those unlabeled data. However, it is time-consuming and expensive. Hence, we do active learning to efficiently querying the data and minimizing the total cost of labeling. Meanwhile, when we observe time series data, we might see a periodic pattern there. As we know that periodicity is one of the general aspects of time series data. By discovering potential periods in time series data, we can get more important information about the data. We might be able to label the data by knowing this information. Here we aim to provide an approach which is able to analyze periodicity in time series data to help active learning to do a better job and achieve higher accuracy using less number of labeled data. Through periodicity analysis, our approach can extract temporal features, select the best unlabeled instances, and label the unlabeled instances. Our approach presents the algorithm to analyze the periodic pattern for handling multi-class time series sequence and provide the state of the art results when combine it with active learning.

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