scholarly journals Multi-Channel Fusion Classification Method Based on Time-Series Data

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4391
Author(s):  
Xue-Bo Jin ◽  
Aiqiang Yang ◽  
Tingli Su ◽  
Jian-Lei Kong ◽  
Yuting Bai
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Short Term Memory ◽  
Evidence Theory ◽  
Recurrence Plot ◽  
Series Data ◽  
Public Datasets ◽  
Application Fields ◽  
Original Time

Time-series data generally exists in many application fields, and the classification of time-series data is one of the important research directions in time-series data mining. In this paper, univariate time-series data are taken as the research object, deep learning and broad learning systems (BLSs) are the basic methods used to explore the classification of multi-modal time-series data features. Long short-term memory (LSTM), gated recurrent unit, and bidirectional LSTM networks are used to learn and test the original time-series data, and a Gramian angular field and recurrence plot are used to encode time-series data to images, and a BLS is employed for image learning and testing. Finally, to obtain the final classification results, Dempster–Shafer evidence theory (D–S evidence theory) is considered to fuse the probability outputs of the two categories. Through the testing of public datasets, the method proposed in this paper obtains competitive results, compensating for the deficiencies of using only time-series data or images for different types of datasets.

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

scholarly journals A Stacked Human Activity Recognition Model Based on Parallel Recurrent Network and Time Series Evidence Theory

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4016
Author(s):  
Peng Zhang ◽  
Zhenjiang Zhang ◽  
Han-Chieh Chao
Keyword(s):  
Time Series ◽  
Human Activity ◽  
Time Series Data ◽  
Short Term Memory ◽  
Wearable Sensors ◽  
Evidence Theory ◽  
Living Condition ◽  
Series Data ◽  
Multidimensional Data ◽  
Posture Analysis

As the foundation of Posture Analysis, recognizing human activity accurately in real time assists in using machines to intellectualize living condition and monitor health status. In this paper, we focus on recognition based on raw time series data, which are continuously sampled by wearable sensors, and a fine-grained evidence reasoning approach has been proposed to produce a timely and reliable result. First, the basic time unit of input data is selected by finding a tradeoff between accuracy and time cost. Then, the approach uses Long Short Term Memory to extract features and project raw multidimensional data into probability assignments, followed by trainable evidence combination and inference network that reduce uncertainly to improve the classification accuracy. Experiments validate the effectiveness of fine granularity and evidence reasoning while the final results indicate that the recognition accuracy of this approach can reach 96.4% with no additional complexity in training.

Faithfulness of Recurrence Plots: A Mathematical Proof

2015 ◽  
Vol 25 (12) ◽  
pp. 1550168 ◽  
Author(s):  
Yoshito Hirata ◽  
Motomasa Komuro ◽  
Shunsuke Horai ◽  
Kazuyuki Aihara
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Mathematical Proof ◽  
Recurrence Plot ◽  
Series Data ◽  
Recurrence Plots ◽  
Original Time Series ◽  
Mild Conditions ◽  
Original Time ◽  
Almost All

It is practically known that a recurrence plot, a two-dimensional visualization of time series data, can contain almost all information related to the underlying dynamics except for its spatial scale because we can recover a rough shape for the original time series from the recurrence plot even if the original time series is multivariate. We here provide a mathematical proof that the metric defined by a recurrence plot [Hirata et al., 2008] is equivalent to the Euclidean metric under mild conditions.

scholarly journals Classification of Variable Stars Light Curves Using Long Short Term Memory Network

2021 ◽  
Vol 8 ◽  
Author(s):  
Saksham Bassi ◽  
Kaushal Sharma ◽  
Atharva Gomekar
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Short Term Memory ◽  
Traditional Approach ◽  
Variable Stars ◽  
Light Curves ◽  
Series Data ◽  
Automated Method ◽  
Lstm Network

Owing to the current and upcoming extensive surveys studying the stellar variability, accurate and quicker methods are required for the astronomers to automate the classification of variable stars. The traditional approach of classification requires the calculation of the period of the observed light curve and assigning different variability patterns of phase folded light curves to different classes. However, applying these methods becomes difficult if the light curves are sparse or contain temporal gaps. Also, period finding algorithms start slowing down and become redundant in such scenarios. In this work, we present a new automated method, 1D CNN-LSTM, for classifying variable stars using a hybrid neural network of one-dimensional CNN and LSTM network which employs the raw time-series data from the variable stars. We apply the network to classify the time-series data obtained from the OGLE and the CRTS survey. We report the best average accuracy of 85% and F1 score of 0.71 for classifying five classes from the OGLE survey. We simultaneously apply other existing classification methods to our dataset and compare the results.

scholarly journals Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Suspended Sediment ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Original Time Series ◽  
Inference System ◽  
Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

G-CNN and double-referenced thresholding for detecting time series anomalies

2020 ◽  
pp. 1-12
Author(s):  
Liping Li ◽  
Zean Tian ◽  
Kenli Li ◽  
Cen Chen
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Prediction Errors ◽  
Feedback Information ◽  
Public Datasets ◽  
Gated Recurrent Units ◽  
Control Feedback ◽  
Intermediate Output ◽  
Internet Company

Anomaly detection based on time series data is of great importance in many fields. Time series data produced by man-made systems usually include two parts: monitored and exogenous data, which respectively are the detected object and the control/feedback information. In this paper, a so-called G-CNN architecture that combined the gated recurrent units (GRU) with a convolutional neural network (CNN) is proposed, which respectively focus on the monitored and exogenous data. The most important is the introduction of a complementary double-referenced thresholding approach that processes prediction errors and calculates threshold, achieving balance between the minimization of false positives and the false negatives. The outstanding performance and extensive applicability of our model is demonstrated by experiments on two public datasets from aerospace and a new server machine dataset from an Internet company. It is also found that the monitored data is close associated with the exogenous data if any, and the interpretability of the G-CNN is discussed by visualizing the intermediate output of neural networks.

scholarly journals High-pass filtering artifacts in multivariate classification of neural time series data

2021 ◽  
Vol 352 ◽  
pp. 109080
Author(s):  
Joram van Driel ◽  
Christian N.L. Olivers ◽  
Johannes J. Fahrenfort
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Multivariate Classification ◽  
High Pass
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