scholarly journals Robust IoT time series classification with data compression and deep learning

2020 ◽  
Vol 398 ◽  
pp. 222-234 ◽  
Author(s):  
Joseph Azar ◽  
Abdallah Makhoul ◽  
Raphaël Couturier ◽  
Jacques Demerjian
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Data Compression ◽  
Time Series Classification

scholarly journals Edge4TSC: Binary Distribution Tree-Enabled Time Series Classification in Edge Environment

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1908
Author(s):  
Chao Ma ◽  
Xiaochuan Shi ◽  
Wei Li ◽  
Weiping Zhu
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Time Series Data ◽  
Series Representation ◽  
Series Data ◽  
Feature Engineering ◽  
Time Series Classification ◽  
Binary Distribution ◽  
New Time

In the past decade, time series data have been generated from various fields at a rapid speed, which offers a huge opportunity for mining valuable knowledge. As a typical task of time series mining, Time Series Classification (TSC) has attracted lots of attention from both researchers and domain experts due to its broad applications ranging from human activity recognition to smart city governance. Specifically, there is an increasing requirement for performing classification tasks on diverse types of time series data in a timely manner without costly hand-crafting feature engineering. Therefore, in this paper, we propose a framework named Edge4TSC that allows time series to be processed in the edge environment, so that the classification results can be instantly returned to the end-users. Meanwhile, to get rid of the costly hand-crafting feature engineering process, deep learning techniques are applied for automatic feature extraction, which shows competitive or even superior performance compared to state-of-the-art TSC solutions. However, because time series presents complex patterns, even deep learning models are not capable of achieving satisfactory classification accuracy, which motivated us to explore new time series representation methods to help classifiers further improve the classification accuracy. In the proposed framework Edge4TSC, by building the binary distribution tree, a new time series representation method was designed for addressing the classification accuracy concern in TSC tasks. By conducting comprehensive experiments on six challenging time series datasets in the edge environment, the potential of the proposed framework for its generalization ability and classification accuracy improvement is firmly validated with a number of helpful insights.

Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning

2020 ◽  
pp. 33-71
Author(s):  
Christian Bock ◽  
Michael Moor ◽  
Catherine R. Jutzeler ◽  
Karsten Borgwardt
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Time Series Classification

scholarly journals DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn

2019 ◽  
Vol 149 ◽  
pp. 91-104 ◽  
Author(s):  
Roberto Interdonato ◽  
Dino Ienco ◽  
Raffaele Gaetano ◽  
Kenji Ose
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Classification ◽  
View Point

scholarly journals Time Series Classification with InceptionFCN

Sensors ◽  
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.

scholarly journals FilterNet: A Many-to-Many Deep Learning Architecture for Time Series Classification

2020 ◽  
Author(s):  
Robert D. Chambers ◽  
Nathanael C. Yoder
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Activity Recognition ◽  
Sensor Data ◽  
Time Series Classification ◽  
Discrete Events ◽  
Segmentation Accuracy ◽  
Model Size ◽  
Classification Tasks

We present and benchmark FilterNet, a flexible deep learning architecture for time series classification tasks, such as activity recognition via multichannel sensor data. It adapts popular CNN and CNN-LSTM motifs which have excelled in activity recognition benchmarks, implementing them in a many-to-many architecture to markedly improve frame-by-frame accuracy, event segmentation accuracy, model size, and computational efficiency. We propose several model variants, evaluate them alongside other published models using the Opportunity benchmark dataset, demonstrate the effect of model ensembling and of altering key parameters, and quantify the quality of the models’ segmentation of discrete events. We also offer recommendations for use and suggest potential model extensions. FilterNet advances the state of the art in all measured accuracy and speed metrics on the benchmarked dataset, and it can be extensively customized for other applications.

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