
A Framework for Predictive Analysis of Stock Market Indices : A Study of the Indian Auto Sector
Analysis and prediction of stock market time series data has attracted c...
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DTWSSE: Data Augmentation with a Siamese Encoder for Time Series
Access to labeled time series data is often limited in the real world, w...
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An analysis of deep neural networks for predicting trends in time series data
Recently, a hybrid Deep Neural Network (DNN) algorithm, TreNet was propo...
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Visualising Deep Network's TimeSeries Representations
Despite the popularisation of the machine learning models, more often th...
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SeismicNet: A Deep Densely Connected Neural Network to Detect Seismic Events
One of the risks of largescale geologic carbon sequestration is the pot...
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SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems
This work proposes a supervised multichannel timeseries learning frame...
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Winning with Simple Learning Models: Detecting Earthquakes in Groningen, the Netherlands
Deep learning is fast emerging as a potential disruptive tool to tackle ...
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Clustering Time Series Data through Autoencoderbased Deep Learning Models
Machine learning and in particular deep learning algorithms are the emerging approaches to data analysis. These techniques have transformed traditional data miningbased analysis radically into a learningbased 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 learningbased 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 twostage 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 autoencoderbased 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 twostage 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.
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