scholarly journals Multivariate Time Series Classification of Sensor Data from an Industrial Drying Hopper: A Deep Learning Approach

2021 ◽  
Author(s):  
Md Mushfiqur Rahman
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Multivariate Time Series ◽  
Sensor Data ◽  
Learning Approach ◽  
Time Series Classification

scholarly journals SSIM—A Deep Learning Approach for Recovering Missing Time Series Sensor Data

2019 ◽  
Vol 6 (4) ◽  
pp. 6618-6628 ◽  
Author(s):  
Yi-Fan Zhang ◽  
Peter J. Thorburn ◽  
Wei Xiang ◽  
Peter Fitch
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Sensor Data ◽  
Learning Approach

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.

scholarly journals XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3137
Author(s):  
Kevin Fauvel ◽  
Tao Lin ◽  
Véronique Masson ◽  
Élisa Fromont ◽  
Alexandre Termier
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Input Data ◽  
State Of The Art ◽  
Multivariate Time Series ◽  
Learning Approach ◽  
Multiple Domains ◽  
Post Hoc

Multivariate Time Series (MTS) classification has gained importance over the past decade with the increase in the number of temporal datasets in multiple domains. The current state-of-the-art MTS classifier is a heavyweight deep learning approach, which outperforms the second-best MTS classifier only on large datasets. Moreover, this deep learning approach cannot provide faithful explanations as it relies on post hoc model-agnostic explainability methods, which could prevent its use in numerous applications. In this paper, we present XCM, an eXplainable Convolutional neural network for MTS classification. XCM is a new compact convolutional neural network which extracts information relative to the observed variables and time directly from the input data. Thus, XCM architecture enables a good generalization ability on both large and small datasets, while allowing the full exploitation of a faithful post hoc model-specific explainability method (Gradient-weighted Class Activation Mapping) by precisely identifying the observed variables and timestamps of the input data that are important for predictions. We first show that XCM outperforms the state-of-the-art MTS classifiers on both the large and small public UEA datasets. Then, we illustrate how XCM reconciles performance and explainability on a synthetic dataset and show that XCM enables a more precise identification of the regions of the input data that are important for predictions compared to the current deep learning MTS classifier also providing faithful explainability. Finally, we present how XCM can outperform the current most accurate state-of-the-art algorithm on a real-world application while enhancing explainability by providing faithful and more informative explanations.

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

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2498 ◽  
Author(s):  
Robert D. Chambers ◽  
Nathanael C. Yoder
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Activity Recognition ◽  
Short Term Memory ◽  
Sensor Data ◽  
Time Series Classification ◽  
Discrete Events ◽  
Segmentation Accuracy ◽  
Model Size

In this paper, 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 convolutional neural network (CNN) and long short-term memory (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 when applied to the benchmarked dataset, and it can be extensively customized for other applications.

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.

Sign in / Sign up

Export Citation Format

Share Document