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Title: Dynamical Clustering of Time Series Data Using Multi-Decoder RNN Autoencoder

Paper content:
Clustering algorithms have wide applications and play an important role in data analysis fields including time series data analysis.
The performance of a clustering algorithm depends on the features extracted from the data.
However, in time series analysis, there has been a problem that the conventional methods based on the signal shape are unstable for phase shift, amplitude and signal length variations.
In this paper, we propose a new clustering algorithm focused on the dynamical system aspect of the signal using recurrent neural network and variational Bayes method.
Our experiments show that our proposed algorithm has a robustness against above variations and boost the classification performance.
The rapid progress of IoT technology has brought huge data in wide fields such as traffic, industries, medical research and so on.
Most of these data are gathered continuously and accumulated as time series data, and the extraction of features from a time series have been studied intensively in recent years.
The difficulty of time series analysis is the variation of the signal in time which gives rise to phase shift, compress/stretch and length variation.
Many methods have been proposed to solve these problems.
Dynamic Time Warping (DTW) was designed to measure the distance between warping signals (Rabiner & Juang, 1993) .
This method solved the compress/stretch problem by applying a dynamic planning method.
Fourier transfer or wavelet transfer can extract the features based on the frequency components of signals.
The phase shift independent features are obtained by calculating the power spectrum of the transform result.
In recent years, the recurrent neural network (RNN), which has recursive neural network structure, has been widely used in time series analysis (Elman, 1990; 1991) .
This recursive network structure makes it possible to retain the past information of time series.
Furthermore, this architecture enables us to apply this algorithm to signals with different lengths.
Although the methods mentioned above are effective solutions for the compress/stretch, phase shift and signal length variation issues respectively, little has been studied about these problems comprehensively.
Let us turn our attention to feature extraction again.
Unsupervised learning using a neural network architecture autoencoder (AE) has been studied as a feature extraction method (Hinton & Salakhutdinov, 2006; Vincent et al., 2008; Rifai et al., 2011) .
AE using RNN structure (RNN-AE) has also been proposed (Srivastava et al., 2015) and it has been applied to real data such as driving data (Dong et al., 2017) and others.
RNN-AE can be also interpreted as the discrete dynamical system: chaotic behavior and the deterrent method have been studied from this point of view (Zerroug et al., 2013; Laurent & von Brecht, 2016) .
In this paper, we propose a new clustering algorithm for feature extraction focused on the dynamical system aspect of RNN-AE.
In order to achieve this, we employed a multi-decoder autoencoder with multiple decoders to describe different dynamical systems.
We also applied the variational Bayes method (Attias, 1999; Ghahramani & Beal, 2001; Kaji & Watanabe, 2011) as the clustering algorithm.
This paper is composed as follows: in Section 4, we explain AE from a dynamical system view, then we define our model and from this, derive its learning algorithm.
In Section 5, we describe the application of our algorithm to an actual time series to show its robustness, including running two experiments using periodic data and driving data.
Finally we summarize our study and describe our future work in Section 7.
We verified the feature extraction peformance of the MDRA using actual time series data.
In Section 5.1, we saw that the periodic signals are completely classified by the frequency using clustering weight r n .
In this experiment, the average clustering weights, the elements of are (3.31e-01, 8.31e-47, 8.31e-47, 3.46e-01, 8.31e-47, 3.19e-01, 8.31e-47) , with only three components having effective weights.
This weight narrowing-down is one of the advantages of VB learning.
The left of Fig. 9 shows an enlarged view of around "freq 4" in Fig. 7 (right) .
We found that the distribution of "freq 4" is in fact spread linearly.
The right of Fig. 9 is the result Hinton, 2008) .
We found that each frequency data formed several spreading clusters without overlapping.
As we saw earlier, the distribution of r n has a spreading distribution compared to that of h n .
We inferred that the spreading of the distribution r n was caused by extracting the diversity on the driving scenes.
In addition, the identification result shows that the combination of the features given by r n and h n can improve the performance.
Dong et al. (2017) , which studied a driver identify algorithm using the AE, proposed the minimization of the error integrating the reconstruction error of AE and the classification error of deep neural network.
This algorithm can avoid the over-fitting by using unlabeled data whose data collection cost is smaller than labeled data.
From these results, we can expect that the MDRA can contribute to not only boost identification performance but also restrain the over-fitting.
In this paper, we proposed a new algorithm MDRA that can extract dynamical system features of a time series data.
We conducted experiments using periodic signals and actual driving data to verify the advantages of MDRA.
The results show that our algorithm not only has robustness for the phase shift, amplitude and signal length variation, but also can boost classification performance.

assistant: text: Novel time series data clustring algorithm based on dynamical system features.