Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification

High-dimensional time series classification is a serious problem. A similarity measure based on distance is one of the methods for time series classification. This paper proposes a metric learning-based univariate time series classification method (ML-UTSC), which uses a Mahalanobis matrix on metric learning to calculate the local distance between multivariate time series and combines Dynamic Time Warping(DTW) and the nearest neighbor classification to achieve the final classification. In this method, the features of the univariate time series are presented as multivariate time series data with a mean value, variance, and slope. Next, a three-dimensional Mahalanobis matrix is obtained based on metric learning in the data. The time series is divided into segments of equal intervals to enable the Mahalanobis matrix to more accurately describe the features of the time series data. Compared with the most effective measurement method, the related experimental results show that our proposed algorithm has a lower classification error rate in most of the test datasets.

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Time Series Classification with InceptionFCN

Sensors ◽

10.3390/s22010157 ◽

2021 ◽

Vol 22 (1) ◽

pp. 157

Author(s):

Saidrasul Usmankhujaev ◽

Bunyodbek Ibrokhimov ◽

Shokhrukh Baydadaev ◽

Jangwoo Kwon

Keyword(s):

Time Series ◽

Deep Learning ◽

Deep Neural Networks ◽

Learning Approaches ◽

Time Series Classification ◽

Convolutional Network ◽

Training Time ◽

Learning Techniques ◽

Significant Research ◽

Previous State

Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last decade, and significant research has been proposed with various solutions, including algorithm-based approaches as well as machine and deep learning approaches. This paper focuses on combining the two well-known deep learning techniques, namely the Inception module and the Fully Convolutional Network. The proposed method proved to be more efficient than the previous state-of-the-art InceptionTime method. We tested our model on the univariate TSC benchmark (the UCR/UEA archive), which includes 85 time-series datasets, and proved that our network outperforms the InceptionTime in terms of the training time and overall accuracy on the UCR archive.

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Time Series Forecasting and Classification Models Based on Recurrent with Attention Mechanism and Generative Adversarial Networks

Sensors ◽

10.3390/s20247211 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7211

Author(s):

Kun Zhou ◽

Wenyong Wang ◽

Teng Hu ◽

Kai Deng

Keyword(s):

Time Series ◽

Short Term Memory ◽

Time Series Classification ◽

Training Process ◽

Short Term ◽

Generative Adversarial Network ◽

Convolutional Network ◽

Term Memory ◽

Adversarial Network ◽

Long Short Term Memory

Time series classification and forecasting have long been studied with the traditional statistical methods. Recently, deep learning achieved remarkable successes in areas such as image, text, video, audio processing, etc. However, research studies conducted with deep neural networks in these fields are not abundant. Therefore, in this paper, we aim to propose and evaluate several state-of-the-art neural network models in these fields. We first review the basics of representative models, namely long short-term memory and its variants, the temporal convolutional network and the generative adversarial network. Then, long short-term memory with autoencoder and attention-based models, the temporal convolutional network and the generative adversarial model are proposed and applied to time series classification and forecasting. Gaussian sliding window weights are proposed to speed the training process up. Finally, the performances of the proposed methods are assessed using five optimizers and loss functions with the public benchmark datasets, and comparisons between the proposed temporal convolutional network and several classical models are conducted. Experiments show the proposed models’ effectiveness and confirm that the temporal convolutional network is superior to long short-term memory models in sequence modeling. We conclude that the proposed temporal convolutional network reduces time consumption to around 80% compared to others while retaining the same accuracy. The unstable training process for generative adversarial network is circumvented by tuning hyperparameters and carefully choosing the appropriate optimizer of “Adam”. The proposed generative adversarial network also achieves comparable forecasting accuracy with traditional methods.

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Auto-adaptive multilayer perceptron for univariate time series classification

Expert Systems with Applications ◽

10.1016/j.eswa.2021.115147 ◽

2021 ◽

Vol 181 ◽

pp. 115147

Author(s):

Felipe Arias del Campo ◽

María Cristina Guevara Neri ◽

Osslan Osiris Vergara Villegas ◽

Vianey Guadalupe Cruz Sánchez ◽

Humberto de Jesús Ochoa Domínguez ◽

...

Keyword(s):

Time Series ◽

Multilayer Perceptron ◽

Time Series Classification ◽

Univariate Time Series

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Channel Attention-Based Temporal Convolutional Network for Satellite Image Time Series Classification

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3095505 ◽

2021 ◽

pp. 1-5

Author(s):

Pengfei Tang ◽

Peijun Du ◽

Junshi Xia ◽

Peng Zhang ◽

Wei Zhang

Keyword(s):

Time Series ◽

Satellite Image ◽

Time Series Classification ◽

Convolutional Network

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Random Subspace Ensembles of Fully Convolutional Network for Time Series Classification

Applied Sciences ◽

10.3390/app112210957 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10957

Author(s):

Yangqianhui Zhang ◽

Chunyang Mo ◽

Jiajun Ma ◽

Liang Zhao

Keyword(s):

Time Series ◽

Deep Learning ◽

Cost Effective ◽

Superior Performance ◽

Series Data ◽

Random Subspace ◽

Time Series Classification ◽

Convolutional Network ◽

Learning Ensembles ◽

Individual Classifier

Time series classification (TSC) task is one of the most significant topics in data mining. Among all methods for this issue, the deep-learning-based shows superior performance for its good adaption to raw series data and automatic extraction of features. However, rare eyes are kept on composing ensembles of these superior individual classifiers to achieve further breakthroughs. The existing deep learning ensembles NNE did a heavy work of combining 60 individuals but did not maximize the deserving improvement, since it merely pays attention to the diversity of individuals but ignores their accuracy. In this paper, we propose to construct an ensemble of Full Convolutional Neural Networks (FCN) by Random Subspace Method (RSM), named RSM-FCN. FCN is a simple but outstanding individual classifier and RSM is suitable for high dimensional data such as time series, but there are few instances. Thus, the combination of these strengths, RSM-FCN provides a highly cost-effective approach to yield promising results. Experiments on the UCR dataset demonstrate the effectiveness and reasonability of the proposed method.

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