Classification Of Time Series Data Research Articles

Data-driven fault detection framework for offshore wind-hydrogen systems

Offshore wind-hydrogen systems operate in harsh marine environments for extended periods, posing risks of low accessibility and high failure rates. This paper proposes a data-driven fault detection framework for offshore wind-hydrogen systems, aiming to promptly detect potential faults based on the system's physical parameters. The research establishes a comprehensive model based on the operating principles of offshore wind-hydrogen systems. The study uses fault injection techniques to simulate common partial faults in offshore wind-hydrogen systems and collects relevant physical parameters (such as wind turbine power generation, energy storage battery charging/discharging current, and electrolyzer hydrogen production rate). A fault detection model was constructed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to enable precise detection of potential faults in the system. The established model provides theoretical support for designing, optimizing, and operating offshore wind-hydrogen systems, reducing reliance on actual experiments and lowering project risks and costs. Introducing artificial faults can evaluate the system's performance in different fault scenarios. This paper proposes a CNN-BiLSTM fault detection method that achieves automatic feature extraction and high-precision classification of time-series fault data, confirming the practicality and feasibility of offshore wind-hydrogen systems. This investigation realizes the integration of the offshore wind-hydrogen system model and deep learning, as well as a fully automated learning process from data to fault detection. By calculating and comparing results, it is verified that the framework has a high accuracy of 96.9%. This innovative research offers new methods and perspectives for enhancing the reliability and efficiency of fault detection in offshore wind-hydrogen systems.

A novel deviation measurement for scheduled intelligent transportation system via comparative spatial-temporal path networks

Transit managers can use Intelligent Transportation System technologies to access large amounts of data to monitor network status. However, the presentation of the data lacks structural information. Existing single-network description technologies are ineffective in representing the temporal and spatial characteristics simultaneously. Therefore, there is a need for complementary methods to address these deficiencies. To address these limitations, this paper proposes an approach that combines Network Snapshots and Temporal Paths for the scheduled system. A dual information network is constructed to assess the degree of operational deviation considering the planning tasks. To validate the effectiveness, discussions are conducted through a modified cosine similarity calculation on theoretical analysis, delay level description, and the ability to identify abnormal dates. Compared to some state-of-the-art methods, the proposed method achieves an average Spearman delay correlation of 0.85 and a relative distance of 3.47. Furthermore, case analyses are invested in regions of China's Mainland, Europe, and the United States, investigating both the overall and sub-regional network fluctuations. To represent the impact of network fluctuations in sub-regions, a response loss value was developed. The times that are prone to fluctuations are also discussed through the classification of time series data. The research can offer a novel approach to system monitoring, providing a research direction that utilizes individual data combined to represent macroscopic states. Our code will be released at https://github.com/daozhong/STPN.git.

Hierarchical and Non- Hierarchical Cluster Classification of Precipitation Time Series Data in Nigeria

Precipitation is highly unpredictable over space and time, and is an indicator for climate change. The present work classified average yearly precipitation over the 36 states and the Federal Capital Territory (FCT) in Nigeria into various groups. The hierarchical and non- hierarchical (k-means) cluster method was used in the classification of 37- years precipitation data (1984-2020). The results showed that six major clusters were formed. The Precipitation across the 36 states and the FCT in Nigeria can be classified as Light precipitation for Sokoto, Borno, Kastina, Yobe and Kano States; Moderate precipitation for Kebbi, Zamfara, Dutse, Yola, Gombe and Bauchi States; Heavy precipitation for Kaduna, Plateau, Kwara, Taraba, Nassarawa, Benue and Kogi States; Very Heavy precipitation for Osun, Ogun, FCT, Ekiti, Niger and Lagos States; Shower for Edo, Abia, Delta, Anambra, Ebonyi, Enugu, Ondo and Oyo States and lastly, Heavy Shower for Akwaibom, Imo, Rivers, Bayelsa and Cross River States.. The rangs of precipitation based on the six classification are 1.5-1.8mm (Light precipitation), 1.9-2.7mm (Moderate precipitation), 2.8-3.6mm (Heavy precipitation), 3.7-4.3mm (Very Heavy precipitation), 4.4-5.4mm (Shower) and greater than 5.5mm (Heavy Shower). The hierarchical cluster method performed better than the non- hierarchical (k-means) cluster method in structuring the precipitation data and shaping it into groups (clusters) such that observations within the same group are more similar than those in different groups. The results of this study can contribute to a better understanding of the climate condition of the36 States and the FCT in Nigeria.

Research on text classification based on neural networks

With the rapid development of information technology, the processing of massive text data has become increasingly important. As a common computer information processing task, text classification has attracted a wide range of research interests. This paper aims to explore the text classification method based on neural network and analyze the key technologies. In order to solve the problem of text time series data classification, this paper uses the text time series data of occupancy detection and applies these neural network models in deep learning, including recurrent neural network, long short-term memory and gated recurrent unit, and trains the neural network through supervised learning. Inputting the room attribute data to these trained neural network models and judging the occupancy of the room. At the same time, observing the experimental results of these neural network models, including training loss, test loss and accuracy, to further study the performance of neural network in processing text time series data classification. This papers experiment aims to evaluate the performance of neural network in text classification and makes a detailed analysis through the experimental results. The goal of the research is to find an effective solution based on neural networks for the classification of text sequence data. Through the analysis of the experimental results, it can be concluded that the method based on neural network is feasible and effective in text sequence data classification. These analysis results will help to further promote the development of text classification technology and provide guidance and reference for practical application.

Effective time-series Data Augmentation with Analytic Wavelets for bearing fault diagnosis

In the realm of rotary machine maintenance, rolling bearings emerge as crucial yet frequently vulnerable components. Ensuring their operational integrity is pivotal for the overall safety and performance of the machinery. Traditional diagnostic methods have utilized a variety of signal processing techniques, ranging from blind source separation to wavelet analysis, and more recently, deep neural networks (DNNs). However, a significant challenge in this domain is the scarcity of labeled failure data in real-world applications, leading to a reliance on data augmentation (DA) techniques to enhance the quality and quantity of the training data. Compounding this problem is the use of DA techniques tailored for images, which are not suitable for the time–frequency representations. This study proposes an innovative Data Augmentation with Analytic Wavelets (DAAW) approach, tailored specifically for time-series (TS) data classification in bearing fault diagnosis. The essence of DAAW lies in its ability to generate synthetic scalogram samples that closely mirror the properties of original samples. The proposed DAAW is directly applied at the input stage of a learning model by successively adjusting predefined parameters to generate scalograms, without the need for auxiliary DA algorithms. This technique is versatile in generating synthetic time–frequency data samples, and the parameterization enables it to be tailored to different time series datasets. Through experimentation with publicly available datasets, we show that combining a simple Convolutional Neural Network (CNN) with the proposed approach results in improved performance. It outperforms existing methods, achieving a mean classification accuracy of 99.49%. The critical distance for average accuracy, as determined by the Nemenyi post-hoc test, is 0.4619. Our results also show enhanced performance with accuracy rates of 90.14%, 78.94%, and 62.86% even under low signal-to-noise ratio (SNR) conditions, using 80%, 60%, and 50% of the dataset, respectively, for model training. Notably, its efficacy remains robust even under challenging low SNR conditions. The proposed DAAW significantly improves classification accuracy in bearing fault diagnosis, simultaneously obviating the need for proxy DA tasks.

Temporal classification of short time series data

MotivationWithin the frame of their genetic capacity, organisms are able to modify their molecular state to cope with changing environmental conditions or induced genetic disposition. As high throughput methods are becoming increasingly affordable, time series analysis techniques are applied frequently to study the complex dynamic interplay between genes, proteins, and metabolites at the physiological and molecular level. Common analysis approaches fail to simultaneously include (i) information about the replicate variance and (ii) the limited number of responses/shapes that a biological system is typically able to take.ResultsWe present a novel approach to model and classify short time series signals, conceptually based on a classical time series analysis, where the dependency of the consecutive time points is exploited. Constrained spline regression with automated model selection separates between noise and signal under the assumption that highly frequent changes are less likely to occur, simultaneously preserving information about the detected variance. This enables a more precise representation of the measured information and improves temporal classification in order to identify biologically interpretable correlations among the data.Availability and implementationAn open source F# implementation of the presented method and documentation of its usage is freely available in the TempClass repository, https://github.com/CSBiology/TempClass [58].

ELM-KL-LSTM: a robust and general incremental learning method for efficient classification of time series data.

Efficiently analyzing and classifying dynamically changing time series data remains a challenge. The main issue lies in the significant differences in feature distribution that occur between old and new datasets generated constantly due to varying degrees of concept drift, anomalous data, erroneous data, high noise, and other factors. Taking into account the need to balance accuracy and efficiency when the distribution of the dataset changes, we proposed a new robust, generalized incremental learning (IL) model ELM-KL-LSTM. Extreme learning machine (ELM) is used as a lightweight pre-processing model which is updated using the new designed evaluation metrics based on Kullback-Leibler (KL) divergence values to measure the difference in feature distribution within sliding windows. Finally, we implemented efficient processing and classification analysis of dynamically changing time series data based on ELM lightweight pre-processing model, model update strategy and long short-term memory networks (LSTM) classification model. We conducted extensive experiments and comparation analysis based on the proposed method and benchmark methods in several different real application scenarios. Experimental results show that, compared with the benchmark methods, the proposed method exhibits good robustness and generalization in a number of different real-world application scenarios, and can successfully perform model updates and efficient classification analysis of incremental data with varying degrees improvement of classification accuracy. This provides and extends a new means for efficient analysis of dynamically changing time-series data.

A constrastive semi-supervised deep learning framework for land cover classification of satellite time series with limited labels

In this work, we present a new semi-supervised learning framework to cope with satellite image time series (SITS) classification in a data paucity scenario, considering extreme low levels of supervision. The proposed methodology, referred as S3ITS (Semi-Supervised Satellite Image Time Series classification method), is based on temporal convolutional neural networks and it takes advantage of both labelled and unlabelled information. S3ITS enforces the data to be projected in a discriminative manifold via contrastive learning, in order to produce a data representation where samples belonging to the same category are closer than the ones belonging to different ones. Pseudo-labelling is employed on unlabelled samples to take the most out of the available unlabelled information. Experiments on two study sites described by SITS of Sentinel-2 images highlight the quality of the proposed method with respect to common SITS-based classification methods and recent machine learning approaches especially tailored for the semi-supervised classification of multi-variate time series data.

A Deep Convolutional Neural Network for Time Series Classification with Intermediate Targets

Deep Convolutional Neural Networks (CNNs) have been successfully used in different applications, including image recognition. Time series data, which are generated in many applications, such as tasks using sensor data, have different characteristics compared to image data, and accordingly, there is a need for specific CNN structures to address their processing. This paper proposes a new CNN for classifying time series data. It is proposed to have new intermediate outputs extracted from different hidden layers instead of having a single output to control weight adjustment in the hidden layers during training. Intermediate targets are used to act as labels for the intermediate outputs to improve the performance of the method. The intermediate targets are different from the main target. Additionally, the proposed method artificially increases the number of training instances using the original training samples and the intermediate targets. The proposed approach converts a classification task with original training samples to a new (but equivalent) classification task that contains two classes with a high number of training instances. The proposed CNN for Time Series classification, called CNN-TS, extracts features depending the distance of two time series. CNN-TS was evaluated on various benchmark time series datasets. The proposed CNN-TS achieved 5.1% higher overall accuracy compared to the CNN base method (without an intermediate layer). Additionally, CNN-TS achieved 21.1% higher average accuracy compared to classical machine-learning methods, i.e., linear SVM, RBF SVM, and RF. Moreover, CNN-TS was on average 8.43 times faster in training time compared to the ResNet method.

Research on linguistic multi-attribute decision making method for normal cloud similarity

Aiming at the uncertainty linguistic transformation problem in multi-attribute decision making, a decision-making method based on normal cloud similarity was proposed. Firstly, starting from the normal cloud characteristic curves, a normal cloud similarity measurement method based on Wasserstein distance is proposed by combing with the normal cloud entropy-containing expectation curve, which is using the Wasserstein distance to characterize the similarity characteristics of probability distribution. The properties of the proposed similarity measure are discussed in the paper. Secondly, the performance of the proposed method is compared and analyzed with the existed methods by numerical simulation experiment and time series data classification experiment. The experimental results show that the proposed method has good similarity discrimination ability, high classification accuracy and low CPU time cost. Finally, the method was successfully applied into linguistic multi-attribute decision making, and TOPSIS thought is used to compare and rank the schemes, so as to realize the final decision.

Freezing of Gait Classification in Parkinson’s Patients Using Machine Learning

This study aims to design personalized machine learning models for the classification of time-series data to detect freezing of gait (FoG) in Parkinsons’ patients in short time intervals. FoG is the medical terminology for sudden episodes of an inability to move in patients that suffer from Parkinson’s disease. Data collected experimentally by Xuanwu Hospital were used. Each 0.002-second interval is labeled as FoG positive or negative by physicians. Using information gain statistics, it was determined that out of 58 features of accelerometer, EEG, and EMG measurements, 35 measurements provide the most information about FoG presence. Features were normalized via z-score normalization. For feature vectors, data are grouped into 0.5-second batches with .002 second timeframes for LSTM; while data are grouped into 0.5-second intervals for other models. The FoG positive/negative classes were balanced through SMOTE. All models were hyperparameter trained through 10-fold cross-validation. The F-1 scores of LSTM, Random Forest, SVM, Decision Tree, and Logistic Regression are 89.71%, 89.69%, 87.00%, 74.44%, 67.21% respectively. Of the models analyzed, LSTM has the highest recall at 93.16%, while Random Forest has the highest precision at 94.34%. LSTM detects the most positive instances, while Random Forest has precise detection. LSTM has a higher F-1 score, indicating it is better at balancing precision and recall. These personalized short interval-input models can be implemented in wearable devices to detect freezing of gait to aid physicians’ assessment of disease severity and treatment.

CLS Token Additional Embedding Method Using GASF and CNN for Transformer based Time Series Data Classification Tasks

CLS Token Additional Embedding Method Using GASF and CNN for Transformer based Time Series Data Classification Tasks

DEEP LEARNING FOR SIMULTANEOUS IMPUTATION AND CLASSIFICATION OF TIME SERIES INCOMPLETE DATA

Classification with time series data has many practical applications in finance, medicine, manufacturing, energy, transportation, and agriculture. However, time series data is often plagued by missing values, which not only decreases the accuracy but also increases the complexity of classification models. A common approach to address classification with missing data is to use imputation methods to estimate the missing values before constructing classification models. Recurrent neural network models have been shown to be effective in estimating missing values for time series data. On the other hand, convolutional neural network models have demonstrated their effectiveness in building classification models for time series data. However, there has been no research that specifically addresses how to combine these two types of models to simultaneously address the problem of time series classification with incomplete data. Therefore, in this paper, a model combining recurrent neural networks and convolutional neural networks is proposed to build a model that can simultaneously estimate missing values and classify time series data. The experimental results demonstrate that the proposed model performs better than the existing methods for time series classification with incomplete data.

LSTMAE-DWSSLM: A unified approach for imbalanced time series data classification

LSTMAE-DWSSLM: A unified approach for imbalanced time series data classification

A First Application of Machine and Deep Learning for Background Rejection in the ALPS II TES Detector

AbstractAxions and axion‐like particles are hypothetical particles predicted in extensions of the standard model and are promising cold dark matter candidates. The Any Light Particle Search (ALPS II) experiment is a light‐shining‐through‐the‐wall experiment that aims to produce these particles from a strong light source and magnetic field and subsequently detect them through a reconversion into photons. With an expected rate ≈1 photon per day, a sensitive detection scheme needs to be employed and characterized. One foreseen detector is based on a transition edge sensor (TES). Here, the machine and deep learning algorithms for the rejection of background events recorded with the TES are investigated. A first application of convolutional neural networks to classify time series data measured with the TES is also presented.

EBi-LSTM: an enhanced bi-directional LSTM for time-series data classification by heuristic development of optimal feature integration in brain computer interface

Generally, time series data is referred to as the sequential representation of data that observes from different applications. Therefore, such expertise can use Electroencephalography (EEG) signals to fetch data regarding brain neural activities in brain–computer interface (BCI) systems. Due to massive and myriads data, the signals are appealed in a non-stationary format that ends with a poor quality resolution. To overcome this existing issue, a new framework of enhanced deep learning methods is proposed. The source signals are collected and undergo feature extraction in four ways. Hence, the features are concatenated to enhance the performance. Subsequently, the concatenated features are given to probability ratio-based Reptile Search Algorithm (PR-RSA) to select the optimal features. Finally, the classification is conducted using Enhanced Bi-directional Long Short-Term Memory (EBi-LSTM), where the hyperparameters are optimized by PR-RSA. Throughout the result analysis, it is confirmed that the offered model obtains elevated classification accuracy, and thus tends to increase the performance.

Wheel Defect Detection with Advanced Machine Learning

Abstract: Wheel defects on railway wagons have been identified as an important source of damage to the railway infrastructure and rolling stock. They also cause noise and vibration emissions that are costly to mitigate. We propose two machine learning methods to automatically detect these wheel defects, based on the wheel vertical force measured by a permanently installed sensor system on the railway network. Our methods automatically learn different types of wheel defects and predict during normal operation if a wheel has a defect or not. The first method is based on novel features for classifying time series data and it is used for classification with a support vector machine. To evaluate the performance of our method we construct multiple data sets for the following defect types: flat spot, shelling, and non-roundness. We outperform classical defect detection methods for flat spots and demonstrate prediction for the other two defect types for the first time. Motivated by the recent success of artificial neural networks for image classification, we train custom artificial neural networks with convolutional layers on 2-D representations of the measurement time series. The neural network approach improves the performance on wheels with flat spots and non-roundness by explicitly modelling the multi sensor structure of the measurement system through multiple instances learning and shift invariant networks.

Classification of Time Series Data Obtained by the Satellite by Using Rule-Based and Machine-Learning Methods

Classification of Time Series Data Obtained by the Satellite by Using Rule-Based and Machine-Learning Methods

Fuzzy support vector machine for classification of time series data: A simulation study

Support vector machine (SVM) has become one of most developed methods for classification, focusing on cross-sectional analysis. However, classification of time series data is an important issue in statistics and data mining. Classification of time series data using SVMs that focus on cross-sectional data leads to improper classification, and hence, the SVM needs to be extended for handling time series dataset. As with cross-section data, the problem of imbalanced data is also common in time series data. Fuzzy method has been proven to be capable of overcoming the case of imbalanced data. In this paper, we developed a Fuzzy Support Vector Machine (FSVM) model to classify time series data with imbalanced class. The proposed method puts the fuzzy membership function on the constraint function. Through simulation studies, this research aims to assess the performance of the developed FSVM in classifying time series data. Based on the classification accuracy criteria, we prove that the proposed FSVM method outperforms the standard SVM method for the classification of multiclass time series data.

A Novel Default Risk Prediction and Feature Importance Analysis Technique for Marketplace Lending using Machine Learning

Marketplace lending has fundamentally changed the relationship between borrowers and lenders in financial markets. As with many other financial products that have emerged in recent years, internet-based investors may be inexperienced in marketplace lending, highlighting the importance of forecasting default rates and evaluating default features such as the loan amount, interest rates, and FICO score. Potential borrowers on marketplace lending platforms may already have been rejected by banks as too risky to lend to, which amplifies the problem of asymmetric information. This paper proposes a holistic data processing flow for the loan status classification of marketplace lending multivariate time series data by using the Bidirectional Long Short-Term Memory model (BiLSTM) to predict “non-default,” “distressed,” and “default” loan status, which outperforms conventional techniques. We adopt the SHapely Additive exPlanations (SHAP) and a four-step ahead model, allowing us to extract the most significant features for default risk assessment. Using our approach, lenders and regulators can identify the most relevant features to enhance the default risk assessment method over time in addition to early risk prediction.