An autoencoder-based deep learning approach for clustering time series data

Abstract

This paper introduces a two-stage deep learning-based methodology for clustering time series data. First, a novel technique is introduced to utilize the characteristics (e.g., volatility) of the given time series data in order to create labels and thus enable transformation of the problem from an unsupervised into a supervised learning. Second, an autoencoderbased deep learning model is built to model both known and hidden non-linear features of time series data. The paper reports a case study in which the selected financial and stock time series data of over 70 stock indices are clustered into distinct groups using the introduced two-stage procedure. The results show that the proposed methodology is capable of achieving 87.5% accuracy in clustering and predicting the labels for unseen time series data. The paper also reports an important finding in which it is observed that the performance of both techniques (i.e., autoencoder and Kmeans) are comparable. However, there are a few instances of time series data that are classified differently by the autoencoder-based methodology compared to the Kmeans algorithm. The results may indicate that the proposed deep learning-based approach is taking into account additional hidden features that might be overlooked by conventional Kmeans. The finding raises the question whether the explicit features of data should be analyzed for clustering or more advanced techniques such as deep learning need to be adapted by which hidden features and relationships are explored for clustering purposes.

Fake Reviews Detection through Ensemble Learning

“…The focus of this work was on analyzing static texts. In more challenging settings, when there are interactions between two parties (e.g., chatting and lie detection), more sophisticated analysis would be needed to take into account the time dimension (i.e., a time series problem) such as Long Short-Term Memory [18], [19] and clustering using deep learning [20], reinforcement learning [21], and evidence theory [22].…”

Fake Reviews Detection through Analysis of Linguistic Features

“…The feature-based detection of fake reviews can be also applicable to similar problems such as malware detection [28] that needs to be explored. It is also possible to reduce the number of linguistic features using deep learning techniques such as autoencoders and identify the most significant and influential features to build the model [29]. An interesting approach to detecting fake reviews can be the adaptation of reinforcement learning to this problem where an agent learns the known and latent features that account for reviews being trustworthy or counterfeit reviews [30].…”

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

“…The performance of the developed technique was assessed by evaluating the impact of the size of the input, the depth of the structure, and the constraint parameters such as sparsity and denoising. Considering that the frequency spectrum reflects the frequency distribution of the data, the authors in [95] introduced the time series frequency spectrum as the input to the SAE network. In [96], a novel SAE-based multiple FDD approach was developed.…”