  Machine learning and in particular deep learning algorithms are the emerging
approaches to data analysis. These techniques have transformed traditional data
mining-based analysis radically into a learning-based model in which existing
data sets along with their cluster labels (i.e., train set) are learned to
build a supervised learning model and predict the cluster labels of unseen data
(i.e., test set). In particular, deep learning techniques are capable of
capturing and learning hidden features in a given data sets and thus building a
more accurate prediction model for clustering and labeling problem. However,
the major problem is that time series data are often unlabeled and thus
supervised learning-based deep learning algorithms cannot be directly adapted
to solve the clustering problems for these special and complex types of data
sets. To address this problem, this paper introduces a two-stage method for
clustering time series data. First, a novel technique is introduced to utilize
the characteristics (e.g., volatility) of given time series data in order to
create labels and thus be able to transform the problem from unsupervised
learning into supervised learning. Second, an autoencoder-based deep learning
model is built to learn and model both known and hidden features of time series
data along with their created labels to predict the labels of unseen time
series data. The paper reports a case study in which financial and stock time
series data of selected 70 stock indices are clustered into distinct groups
using the introduced two-stage procedure. The results show that the proposed
procedure is capable of achieving 87.5\% accuracy in clustering and predicting
the labels for unseen time series data.
