Multi-dimensional Time Series Data Research Articles

Semantic Segmentation for Change Detection in Satellite Imaging

Change detection is a common and actual problem in the field of remote sensing. The classical approaches using raw pixel information are very sensitive to noise. In this study we propose the usage of additional semantic information for change detection. We use the semantic segmentation methods like geospatial Segment Anything Model and encoder based U-Net to evaluate the predictions and tracing the semantic information as well as raw information in change detection. Later the multidimensional time series data is used via the Vector Autoregression model to predict the future changes in the landscape. The observations which fall out of the prediction interval are considered as the changes in the landscape. The proposed method is evaluated on the dataset of the random locations across the Baltic region. The research is accompanied by the data and reproducible code at Github repository.

Energy landscape analysis and time-series clustering analysis of patient state multistability related to rheumatoid arthritis drug treatment: The KURAMA cohort study.

Rheumatoid arthritis causes joint inflammation due to immune abnormalities, resulting in joint pain and swelling. In recent years, there have been considerable advancements in the treatment of this disease. However, only approximately 60% of patients achieve remission. Patients with multifactorial diseases shift between states from day to day. Patients may remain in a good or poor state with few or no transitions, or they may switch between states frequently. The visualization of time-dependent state transitions, based on the evaluation axis of stable/unstable states, may provide useful information for achieving rheumatoid arthritis treatment goals. Energy landscape analysis can be used to quantitatively determine the stability/instability of each state in terms of energy. Time-series clustering is another method used to classify transitions into different groups to identify potential patterns within a time-series dataset. The objective of this study was to utilize energy landscape analysis and time-series clustering to evaluate multidimensional time-series data in terms of multistability. We profiled each patient's state transitions during treatment using energy landscape analysis and time-series clustering. Energy landscape analysis divided state transitions into two patterns: "good stability leading to remission" and "poor stability leading to treatment dead-end." The number of patients whose disease status improved increased markedly until approximately 6 months after treatment initiation and then plateaued after 1 year. Time-series clustering grouped patients into three clusters: "toward good stability," "toward poor stability," and "unstable." Patients in the "unstable" cluster are considered to have clinical courses that are difficult to predict; therefore, these patients should be treated with more care. Early disease detection and treatment initiation are important. The evaluation of state multistability enables us to understand a patient's current state in the context of overall state transitions related to rheumatoid arthritis drug treatment and to predict future state transitions.

Anomaly Detection Algorithm for Urban Infrastructure Construction Equipment based on Multidimensional Time Series

Safety is the foundation of urban sustainable development. The urban construction and operation process involves a large amount of multidimensional time series data. By detecting anomalies in these multidimensional time subsequences (MTSs), decision support can be provided for early warning of urban construction and operation risks. Considering the complexity of urban infrastructure, there is an urgent need for fast and accurate anomaly detection. This paper proposes a real-time anomaly detection algorithm based on improved distance measurement (RADIM). RADIM retains the relationships between dimensions in multidimensional subsequences, using an Extended Frobenius Norm with Local Weights (EFN_lw) and a Euclidean distance based on multidimensional data (ED_mv) to measure the similarity of MTSs. Moreover, a threshold update mechanism based on First-order Mean Difference (TMFD) is designed to detect real-time anomalies by assessing deviations. This method has been applied to tunnel construction. According to comparative experiments, RADIM exhibits better adaptability, real-time performance, and accuracy in risk warning of tunnel boring machines and construction status.

Short-term photovoltaic power forecasting with feature extraction and attention mechanisms

The uncertainty of weather conditions has always been a major challenge limiting the performance of photovoltaic (PV) power prediction. Enhancing the accuracy and stability of PV power prediction is crucial for optimizing grid operation. In response to this challenge, this study introduces a hybrid model that integrates an attention mechanism with the Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM). This model aims to mitigate the adverse impact of weather variability on the accuracy of PV power prediction by effectively extracting key features from multidimensional time series data. Additionally, through ablation experiments, this research further assesses the contribution of each model component to performance. Validated with actual data collected from a 1 MW PV power station in China, our proposed model demonstrates significant performance advantages compared to eight advanced prediction models. Ablation study results reveal that removing the CNN component led to a 58.2% increase in MAE and a 53.9% increase in RMSE, while the removal of the attention mechanism resulted in an 83.8% increase in maximum error. These findings underscore the substantial enhancement in prediction accuracy achieved through the integration of CNN and BiLSTM, and the introduction of the attention mechanism significantly boosts the model's prediction stability.

Predicting Student Performance in Online Learning: A Multidimensional Time-Series Data Analysis Approach

As an emerging teaching method, online learning is becoming increasingly popular among learners. However, one of the major drawbacks of this learning style is the lack of effective communication and feedback, which can lead to a higher risk of students failing or dropping out. In response to this challenge, this paper proposes a student performance prediction model based on multidimensional time-series data analysis by considering multidimensional data such as students’ learning behaviors, assessment scores, and demographic information, which is able to extract the characteristics of students’ learning behaviors and capture the connection between multiple characteristics to better explore the impact of multiple factors on students’ performance. The model proposed in this paper helps teachers to individualize education for students at different levels of proficiency and identifies at-risk students as early as possible to help teachers intervene in a timely manner. In experiments on the Open University Learning Analytics Dataset (OULAD), the model achieved 74% accuracy and 73% F1 scores in a four-category prediction task and was able to achieve 99.08% accuracy and 99.08% F1 scores in an early risk prediction task. Compared with the benchmark model, both the multi-classification prediction ability and the early prediction ability, the model in this paper has a better performance.

Time Series Feature Selection Method Based on Mutual Information

Time series data have characteristics such as high dimensionality, excessive noise, data imbalance, etc. In the data preprocessing process, feature selection plays an important role in the quantitative analysis of multidimensional time series data. Aiming at the problem of feature selection of multidimensional time series data, a feature selection method for time series based on mutual information (MI) is proposed. One of the difficulties of traditional MI methods is in searching for a suitable target variable. To address this issue, the main innovation of this paper is the hybridization of principal component analysis (PCA) and kernel regression (KR) methods based on MI. Firstly, based on historical operational data, quantifiable system operability is constructed using PCA and KR. The next step is to use the constructed system operability as the target variable for MI analysis to extract the most useful features for the system data analysis. In order to verify the effectiveness of the method, an experiment is conducted on the CMAPSS engine dataset, and the effectiveness of condition recognition is tested based on the extracted features. The results indicate that the proposed method can effectively achieve feature extraction of high-dimensional monitoring data.

Research on the model of martial arts classroom ideological practice for college students based on time-series data analysis

Abstract This study employs multidimensional time-series data analysis to explore college students’ learning behaviors in martial arts civics classes. By analyzing learner interactions in three categories and time-series data from educational videos, we assess student engagement in mastery of knowledge and quantify learning outcomes for a detailed and accurate evaluation of their learning status. Our team has crafted a targeted learning path network that enhances offline teaching approaches by leveraging insights from online learning trends and in-class performance in martial arts civics education. The results show that the most popular and highest-rated course is Military Poetry (4.7 points) based on the analysis of recall data, and more elements of military poetry should be included in the design of teaching content. Student A64 had the lowest online note quality rating of 0.02, which can be hypothesized to indicate that the student’s attitude was not active enough. Online and offline Mixed Martial Arts classroom Civics education had a mean score of 46.856, with a p-value of 0, and there was a significant difference between traditional offline education and online mixed martial arts classroom Civics education. Most of the students’ personal patriotism, professional knowledge, and abilities were significantly enhanced after participating in online and offline mixed martial arts classroom Civic and Political Education.

TCN Multi-time-scale Transformation and Temporal-Attention Neural Network for Monthly Electricity Consumption Forecasting

Background:: For the efficient and secure running of the power industry, accurate monthly electricity projections are crucial. Due to coupling variations and a variety of data resolutions, current approaches are still unable to accurately extract multidimensional time-series data. Objective:: For monthly electricity consumption forecasting, a multi-time-scale transformation and temporal attention neural network for a temporal convolutional network is proposed. Method:: First, a multi-time-scale compression model of temporal convolutional network is proposed, which compresses data on several time scales from different resolutions, such as the economy, weather, and historical load. Second, a multi-source temporal attention module is built to further dynamically extract crucial information. Finally, the decoding-encoding and residual connections' structure contributes to the prediction's improved resilience. Results:: The proposed method was compared with the state-of-the-art monthly load forecasting based on two years of historical data in a certain region, demonstrating its effectiveness. Conclusion:: Through the verification of local historical data, the proposed model was contrasted with cutting-edge monthly load forecasting techniques. The obtained results demonstrate the effectiveness.

The model of vessel trajectory abnormal behavior detection based on graph attention prediction and reconstruction network

The identification of atypical vessel behavior is a crucial factor in ensuring the safety of maritime navigation and enhancing the effectiveness of waterway oversight. The current approach to detecting vessel abnormal behavior lacks consideration for the inter-dependency among different time series and various characteristics associated with abnormal vessel behavior. Additionally, the subjective setting of anomaly detection threshold adversely affects the real-time performance of anomaly detection, resulting in poor detection accuracy. In order to tackle these issues, this paper explores the monitoring and detection mechanism of vessel abnormal behavior based on Graph Attention Prediction and Reconstruction Network. The proposed mechanism employs a sliding window to generate fixed data input. It effectively captures the inter-dependencies among vessel behavior characteristics. Additionally, it dynamically adjusts the threshold for identifying anomalies based on variations in the marine environment, utilizing the Peak Over Threshold (POT) method. In order to achieve real-time detection of abnormal vessel behavior in multivariate, multi-dimensional time series data, it is necessary to complete the task. The results of the paper are derived from an analysis of the AIS dataset encompassing vessel movements between Dalian and Yantai. The temporal scope of the dataset spans from November 1, 2021, to December 31, 2021. The detection accuracy, detection precision and recall rate are 98.29%, 92.44%, 87.09% respectively. The results indicate that there is an enhancement in the accuracy of detecting abnormal behavior in vessels, leading to a significant reduction in the blind area of VTS (Vessel traffic services) system monitoring.

Research on Fresh Produce Sales Prediction Algorithm for Store Based on Multidimensional Time Series Data Analysis

INTRODUCTION: Fresh produce is a daily necessity; however, offline stores often rely on personal experience for purchase, which is highly subjective and may result in inaccurate estimation of purchase quantities. This can lead to produce wastage and subsequently impact the profitability of business. This paper introduces a fresh produce sales prediction model, which can predict fresh produce sales by analyzing multidimensional time series data that influence sales. This model aims to provide guidance for fresh produce purchase in offline stores.OBJECTIVES: The purpose of this study is to predict fresh produce sales by analyzing multidimensional time series data that influence sales. This aims to provide a basis for fresh produce purchase in stores, reduce produce wastage, and enhance business profitability.METHODS: This paper proposes a fresh produce sales prediction model by analyzing multidimensional time series data that affect store sales of fresh produce. An essential component of this model is the ARIMA-LSTM combined prediction model. In this study, the weighted reciprocal of errors averaging method is selected as the weight determination method for the ARIMA-LSTM combined prediction model.RESULTS: In this paper, the ARIMA-LSTM combined model is used for prediction in two scenarios: when the single-model prediction accuracy is superior and when it is inferior. Experimental results indicate that in the case of lower accuracy in single-model prediction, the combined prediction model outperforms, improving prediction accuracy by 3.86% as measured by MAPE. Comparative experiments are conducted between the fresh produce sales prediction model proposed in this paper and time series prediction framework Prophet, traditional LSTM model, and ARIMA model. The experimental results indicate that the proposed model outperforms the others.CONCLUSION: The fresh produce sales prediction model proposed in this paper is based on multidimensional time series data to predict fresh produce sales in stores. This model can accurately predict the sales of fresh produce, providing purchase guidance for fresh produce stores, reducing fresh produce wastage caused by subjective purchasing factors, and increase business profits.

Research on online and offline double-loop teaching of college students’ mental health education based on cluster analysis algorithm

Abstract In this paper, a knowledge difficulty clustering algorithm (MIBKPC) based on multidimensional time-series data and learning path networks is proposed. The algorithm integrates the cyclic learning sequence, forgetting behavior and system interaction degree formed by learners learning some of the more difficult knowledge points, and constructs a computational model for the difficulty similarity of knowledge points based on system interaction behavior so as to better portray the difficulty similarity of knowledge points based on system interaction behavior. Then a knowledge point difficulty clustering algorithm (MFSKPC) based on multidimensional time-series data and maximum frequent subgraphs is proposed. The algorithm extracts the maximum frequent subgraphs of the atlas based on the learner’s directed learning path atlas using the gSpan-based maximum frequent subgraph mining algorithm and portrays the difficulty similarity of knowledge points based on system interaction behaviors by combining the system interaction degree and the maximum frequent subgraphs. In the accuracy comparison, the clustering accuracy of the MFSKPC algorithm for advanced learners was higher than that of the MIBKPC algorithm for both K=3 and K =5 conditions by 7.89% and 8.29%, respectively. In the comparative analysis of double-loop psychological instruction, the experimental class improved its pre and post-test scores by an average of 2.38 points, while the control group improved by an average of only 1.88 points. The experiment showed that the double-loop teaching based on the MFSKPC algorithm was more effective.

Dynamic mode decomposition with memory.

This study proposed a numerical method of dynamic mode decomposition with memory (DMDm) to analyze multidimensional time-series data with memory effects. The memory effect is a widely observed phenomenon in physics and engineering and is considered to be the result of interactions between the system and environment. Dynamic mode decomposition (DMD) is a linear operation-based, data-driven method for multidimensional time-series data proposed in 2008. Although DMD is a successful method for time-series data analysis, it is based on ordinary differential equationsand thus cannot incorporate memory effects. In this study, we formulated the abstract algorithmic structure of DMDm and demonstrate its utility in overcoming the memoryless restriction imposed by existing DMD methods on the time-evolution model. In the numerical demonstration, we utilized the Caputo fractional differential to implement an example of DMDm such that the time-series data could be analyzed with power-law memory effects. Thus, we developed a fractional DMD, which is a DMD-based method with arbitrary (real value) order differential operations. The proposed method was applied to synthetic data from a set of fractional oscillators and model parameters were estimated successfully. The proposed method is expected to be useful for scientific applications and aid in model estimation, control, and failure detection of mechanical, thermal, and fluid systems in factory machines, such as modern semiconductor manufacturing equipment.

Research on prefecture-level city’s birth rate prediction based on AE-LSTM network

In recent years, China's birth rate has continued to decline, leading to a series of population and economic problems. Although the country has gradually liberalized its Family Planning Policy and started implementing a Three-Child Policy with supporting fertility measures, it has not significantly increased the birth rate. To further implement the policy of encouraging childbirth at the prefecture-level city, this article proposes an AE-LSTM model for predicting the birth rate of the prefecture-level city, combining multidimensional time-series data that affect the birth rate of the population, which achieves accurate prediction of the birth rate of the prefecture-level city, providing a certain decision reference for prefecture-level city governments to formulate more accurate and reasonable childbirth encouragement policies. At the same time, experiment results show that the method proposed in this article has higher accuracy and better generalization performance compared with other mainstream methods.

Generating synthetic multidimensional molecular time series data for machine learning: considerations

The use of synthetic data is recognized as a crucial step in the development of neural network-based Artificial Intelligence (AI) systems. While the methods for generating synthetic data for AI applications in other domains have a role in certain biomedical AI systems, primarily related to image processing, there is a critical gap in the generation of time series data for AI tasks where it is necessary to know how the system works. This is most pronounced in the ability to generate synthetic multi-dimensional molecular time series data (subsequently referred to as synthetic mediator trajectories or SMTs); this is the type of data that underpins research into biomarkers and mediator signatures for forecasting various diseases and is an essential component of the drug development pipeline. We argue the insufficiency of statistical and data-centric machine learning (ML) means of generating this type of synthetic data is due to a combination of factors: perpetual data sparsity due to the Curse of Dimensionality, the inapplicability of the Central Limit Theorem in terms of making assumptions about the statistical distributions of this type of data, and the inability to use ab initio simulations due to the state of perpetual epistemic incompleteness in cellular/molecular biology. Alternatively, we present a rationale for using complex multi-scale mechanism-based simulation models, constructed and operated on to account for perpetual epistemic incompleteness and the need to provide maximal expansiveness in concordance with the Maximal Entropy Principle. These procedures provide for the generation of SMT that minimizes the known shortcomings associated with neural network AI systems, namely overfitting and lack of generalizability. The generation of synthetic data that accounts for the identified factors of multi-dimensional time series data is an essential capability for the development of mediator-biomarker based AI forecasting systems, and therapeutic control development and optimization.

Wind Turbine Blade Early Fault Detection With Faulty Label Unknown and Labeling Bias

In practical industrial applications, the need for a large number of accurately labeled training samples is a significant challenge for fault detection tasks. However, labeling all training samples is expensive and prone to labeling errors, especially for early fault detection of wind turbine blades. This paper proposes a labeling bias (LB) hypothesis. Assuming the labeler only needs to label parts of normal samples that are easy to judge, we design a probability ratio least-squares importance fitting (PRL-SIF) method based on variable homogeneity. Unlike other state-of-the-art positive unlabeled (PU) learning methods, PRL-SIF does not require knowledge of the class priors to achieve training. Furthermore, to better handle the multi-dimensional time-series data of wind turbines, we provide a data preprocessing method based on functional analysis to achieve time series feature extraction and dimensionality reduction. The effectiveness and robustness of the proposed method are verified on 23 real-world wind turbine datasets. Experimental results show that the proposed method can achieve nearly 90% accuracy while only labeling 20% of normal samples.

Koopman-inspired approach for identification of exogenous anomalies in nonstationary time-series data

In many scenarios, it is necessary to monitor a complex system via a time-series of observations and determine when anomalous exogenous events have occurred so that relevant actions can be taken. Determining whether current observations are abnormal is challenging. It requires learning an extrapolative probabilistic model of the dynamics from historical data, and using a limited number of current observations to make a classification. We leverage recent advances in long-term probabilistic forecasting, namely Deep Probabilistic Koopman, to build a general method for classifying anomalies in multi-dimensional time-series data. We also show how to utilize models with domain knowledge of the dynamics to reduce type I and type II error. We demonstrate our proposed method on the important real-world task of global atmospheric pollution monitoring, integrating it with NASA’s Global Earth Observing System Model. The system successfully detects localized anomalies in air quality due to events such as COVID-19 lockdowns and wildfires.

Querying Similar Multi-Dimensional Time Series with a Spatial Database

Similar time series search is one of the most important time series mining tasks in our daily life. As recent advances in sensor technologies accumulate abundant multi-dimensional time series data associated with multivariate quantities, it becomes a privilege to adapt similar time series searches for large-scale and multi-dimensional time series data. However, traditional similar time series search methods are mainly designed for one-dimensional time series, while advanced methods applicable for multi-dimensional time series data are largely immature and, more importantly, are not friendly to users from the domain of geography. As an alternative, we propose a novel method to search similar multi-dimensional time series with spatial databases. Compared with traditional methods that often conduct the similarity search based on features of the raw time series data sequence, the proposed method stores multi-dimensional time series as spatial objects in a spatial database, and then searches similar time series based on their spatial features. To demonstrate the validity of the proposed method, we analyzed the correlation between temporal features of the raw time series and spatial features of their corresponding spatial objects theoretically and empirically. Results indicate that the proposed method can not only support similar multi-dimensional time series searches but also markedly improve its efficiency under many specific scenarios. We believe that such a new paradigm will shed further light on the similarity search in large-scale multi-dimensional time series data, and will lower the barrier for users familiar with spatial databases to conduct complex time series mining tasks.

M3GAN: A masking strategy with a mutable filter for multidimensional anomaly detection

With the advent of the big data era, the detection of anomalies in time series data, especially multidimensional time series data, has received a great deal of attention from researchers in many fields, e.g., industry, medicine, and aerospace. Multidimensional time series anomaly detection (MTSAD) methods aim to discover anomalies. They play an important early warning role in protecting people from possible property damage and personal injury. Many current GAN-based anomaly detection methods suffer from poor robustness, poor generalization, and low accuracy. The M2GAN (a GAN framework based on a masking strategy for multidimensional anomaly detection) proposed in this paper uses the masking method to diversify the data and improve the robustness of the model. This paper further proposes the M3GAN (M2GAN with a mutable filter), which can utilize the different characteristics of different filters to select a suitable model for the features of the data, thus enhancing the generalization ability and improving the accuracy. The experiments prove the effectiveness of the proposed method.

HDF5eis: A storage and input/output solution for big multidimensional time series data from environmental sensors

Modern high-performance computing (HPC) tasks overwhelm conventional geophysical data formats. We describe a new data schema called HDF5eis (read H-D-F-size) for handling big multidimensional time series data from environmental sensors in HPC applications and implement a freely available Python application programming interface (API) for building and processing HDF5eis files. HDF5eis augments the popular Hierarchical Data Format 5 with a minimal set of additional conventions that facilitate fast and flexible data input and output protocols for regularly sampled (in time) data with any number of dimensions. HDF5eis supports arbitrary ancillary data (e.g., metadata) storage in columnar format or as UTF-8 encoded byte streams alongside time series data. Our HDF5eis API enables simple and efficient access to big data sets distributed across a potentially large number of small heterogeneous files through a single point of access. HDF5eis outperforms conventional seismic data formats by up to two orders of magnitude in terms of random read access times. We contribute HDF5eis as an operational tool and an experimental draft proposal that will help establish the next generation of data standards in the earth sciences.

Mental-state estimation model with time-series environmental data regarding cognitive function

It is expected that understanding and estimating the human mental state will be helpful for mental health measures, improving learning and labor work efficiency, and preventing human error. Our research group has been developing a methodology and model for estimating the mental state of humans in their environment from indoor environmental data regarding cognitive function obtained by wireless sensor network technology. This study constructed a model to estimate mental states from multidimensional time-series indoor environmental data such as temperature, humidity, and illumination. The experiment results show that the proposed system shows higher estimation accuracy. In particular, the CNN-LSTM model with multidimensional time-series indoor environmental data shows about 90% estimation accuracy. The results obtained in this study, showing that mental states can be determined with high accuracy from environmental data, are helpful for future research approaches. It may also contribute to the creation of a less stressful environment.