Characteristics Of Time Series Research Articles

Construction of a mental health risk model for college students with long and short-term memory networks and early warning indicators

Abstract With the increasing social pressure and academic competition, the mental health (for convenience, abbreviated as MH) problems of college students are becoming increasingly prominent, but there are often challenges that are difficult to accurately predict and intervene in a timely manner. The aim of this article is to address the early warning needs of college students’ MH problems and construct a model that can timely identify the MH problems of college students. The experiment collected MH related data from college students in S city, and analyzed and trained these data using the Long short-term memory (LSTM) network model. By changing the number of hidden layers, learning rate, batch size, and epoch times, the most suitable training effect was achieved. By using the time-series characteristics of the LSTM model, the selected parameters from the experiment can better capture the changing trends of college students’ MH status, thereby improving prediction accuracy. Finally, three stage indicators of low, medium, and high were set up for early warning of the predicted results, in order to effectively and timely take measures. The research results indicated that the constructed model achieved a minimum regularization loss of 0.0674 after training. Finally, the adjusted model was used to predict the test set, with an average accuracy of 0.852 and an average accuracy of 0.906. The LSTM-based MH risk model performed well in predicting college students’ MH problems and could identify potential risk factors in a timely manner.

A Method for Predicting Transformer Oil-Dissolved Gas Concentration Based on Multi-Window Stepwise Decomposition with HP-SSA-VMD-LSTM

Predicting the concentration of dissolved gases in transformer oil is a critical activity for the early detection of potential faults. To address the prevalent issue of data leakage in current prediction methods, this paper proposes a prediction method that completely avoids data leakage. First, the Hodrick Prescott (HP) filter is used for stepwise decomposition to obtain the long-term trend and high-frequency periodic component. The high-frequency periodic component is further decomposed using singular spectrum analysis (SSA) to extract periodic features. Dispersion entropy (DE) and fuzzy entropy (FE) are utilized alongside the HP and SSA methods to determine the optimal decomposition windows during the process, enhancing the ability of the model to acquire time series features. Then, variational mode decomposition (VMD) is applied to remove noise from the high-frequency component. Finally, the long short-term memory network (LSTM) is employed to predict each decomposed component, and the network parameters undergo optimization through the sparrow search optimization algorithm (SSOA). The two case studies in this work verify that the proposed model excels over other prediction means, providing strong support for subsequent fault prediction.

Spatiotemporal factor models for functional data with application to population map forecast

The proliferation of mobile devices has led to the collection of large amounts of population data. This situation has prompted the need to utilize this rich, multidimensional data in practical applications. In response to this trend, we have integrated functional data analysis (FDA) and factor analysis to address the challenge of predicting hourly population changes across various districts in Tokyo. Specifically, by assuming a Gaussian process, we avoided the large covariance matrix parameters of the multivariate normal distribution. In addition, the data were both time and spatially dependent between districts. To capture various characteristics, a Bayesian factor model was introduced, which modeled the time series of a small number of common factors and expressed the spatial structure through factor loading matrices. Furthermore, the factor loading matrices were made identifiable and sparse to ensure the interpretability of the model. We also proposed a Bayesian shrinkage method as a systematic approach for factor selection. Through numerical experiments and data analysis, we investigated the predictive accuracy and interpretability of our proposed method. We concluded that the flexibility of the method allows for the incorporation of additional time series features, thereby improving its accuracy.

Spatial weighting EMD-LSTM based approach for short-term PM2.5 prediction research

Given the significant health and environmental risks posed by atmospheric PM2.5 pollution, accurately predicting its concentration changes is especially important. Current models fall short in researching time-series feature extraction from pollutants and spatial correlations among monitoring stations. In this study, a spatiotemporal prediction model is introduced to address these issues. The model combines spatial weighting, empirical mode decomposition (EMD), and a long short-term memory (LSTM) network. First, weights are allocated to sites using Pearson correlation analysis and distance weighting methods. Next, the pollutant time series is decomposed using the EMD method. The highly correlated intrinsic mode function (IMF) component is selected for signal reconstruction, enhancing denoising. Finally, the model uses an LSTM network to capture nonlinear and dynamic time series traits, which significantly improves the PM2.5 prediction accuracy. The model utilizes data collected from 10 monitoring stations across Hefei city during 2018-2019, employing the previous 24 h of observations to forecast PM2.5 concentrations for the subsequent hour. By comparing with RNN, HPO-RNN, GRU, LSTM, and CBAM-CNN-Bi LSTM, the results show that our model surpasses five benchmark models in terms of prediction accuracy. Relative to the best-performing CBAM-CNN-Bi LSTM model, our model reduces RMSE and MAE by 73.91% and 72.99%, respectively, and improves R2 by 8.15%. In summary, the proposed spatial weighting EMD-LSTM model offers an efficient new approach for predicting atmospheric PM2.5 pollution. It integrates spatial and time series analysis, significantly enhancing the prediction accuracy.

Application of artificial bee colony algorithm based on homogenization mapping and collaborative acquisition control in network communication security.

In order to optimize the spectrum allocation strategy of existing wireless communication networks and improve information transmission efficiency and data transmission security, this study uses the independent correlation characteristics of chaotic time series to simulate the collection and control strategy of bees, and proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection control. Furthermore, it proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection and control. The method begins by establishing a composite system of uniformly distributed Chebyshev maps. In the neighborhood intervals where the nectar sources are firmly connected and relatively independent, the algorithm then conducts a chaotic traversal search. The research results demonstrated the great performance of the suggested algorithm in each test function as well as the positive effects of the optimization search. The network throughput rate was over 300 kbps, the quantity of security service eavesdropping was below 0.1, and the spectrum utilization rate of the algorithm-based allocation method could be enhanced to 0.8 at the most. Overall, the performance of the proposed algorithm outperformed the comparison algorithm, with high optimization accuracy and a significant amount of optimization. This is favorable for the efficient use of spectrum resources and the secure transmission of communication data, and it encourages the development of spectrum allocation technology in wireless communication networks.

Interpretable and efficient RUL prediction of turbofan engines using EM-enhanced Bi-LSTM with TCN and attention mechanism

Remaining useful life (RUL) prediction for turbofan engines is important in prognostics and health management (PHM) for the maintenance and operation of critical equipment. With continuous innovations in deep learning techniques, the complexity of models continues to increase, but the interpretability and comprehensibility of the prediction results become particularly important in industrial applications. Therefore, in this study, an improved bidirectional long and short-term memory network (Bi-LSTM) based interpretable hybrid deep learning model for RUL prediction of turbofan engines is proposed, which ingeniously integrates time series convolutional networks (TCNs), expectation maximization (EM), Bi-LSTMs, and attention mechanisms. By capturing time-series features at different levels, the model adapts to the complex dynamics of turbofan engine performance evolution in an efficient and cost-effective manner. Experimental validation on the C-MAPSS dataset demonstrated that the model significantly outperforms other methods in terms of RUL prediction performance, especially in improving prediction accuracy and coping with the degradation of complex system dynamics. The largest contribution of key metrics to the model is validated through consistent results from multiple interpretable tools, providing comprehensive and consistent support for understanding and trusting prediction results in industrial applications. This study further enhances the robustness of the model and the reliability of the interpretable results by delving into the dynamic relationships between the properties of the different life stages, which not only reveal the importance of these characteristics in engine life prediction but also provide more comprehensive information about the engine performance variations by observing the dynamic relationships.

Advection-based multiframe iterative correction on PIV fields for improved pressure estimation

A novel method to improve the accuracy of pressure field estimation for advection-dominated flows from time-resolved Particle Image Velocimetry (PIV) data is proposed. Leveraging an advection model we can propagate in time velocity fields from each snapshot. The multiple time-series obtained from time propagation are used to smooth the nonphysical features in the original time-series. This correction is done iteratively. The technique reduces spatial noise by leveraging temporal information while simultaneously correcting temporal jitters using spatial data. Two validation methods are proposed to evaluate the method without knowing the true value of the pressure field. In this paper we provide a proof of the concept using a 3D synthetic dataset based on channel flow and a 2D PIV data set on the wake of a wing foil. Various noise models are tested by superimposing them onto the synthetic dataset, including Gaussian white noise and errors with spatial coherence. The results demonstrate that the proposed method outperforms conventional filters in improving the velocity and pressure fields from noisy velocity data, and in suppressing time jittering of the estimated pressure fields.

Carbon price forecasting using leaky integrator echo state networks with the framework of decomposition-reconstruction-integration

Carbon trading is one of the strategies to achieve the goal of dual carbon. However, carbon prices exhibit non-stationary and nonlinear characteristics, posing significant challenges for accurate forecasting. Inspired by the ideas of “division and conquest” and “granularity reconstruction”, a decomposition-reconstruction-integration framework is built for carbon price forecasting. The proposed model leverages a comprehensive ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sample entropy (SE) to extract features from two perspectives of time and complexity, and thus the one-dimensional time series of carbon price is reconstructed into different items. Subsequently, leaky integrator echo state networks (LiESNs) are utilized to forecast each of these items individually. These individual forecasts are then integrated to generate the final point forecast. To evaluate the uncertainty of the point forecast, a Gaussian process model is applied to interval forecast. The proposed model comprehensively considers the time series features of carbon price including the temporal modal features in the decomposition stage, the entropy probability features in the reconstruction stage, and the essential temporal dependencies via dynamic reservoir of interconnected neurons in the integration forecasting stage. Experimental results demonstrate the model's exceptional forecasting performance, surpassing the effectiveness of alternative approaches.

Ultrasound fuzzy entropy imaging based on time-series signal for tissue characterization

This study proposed a parametric imaging method of fuzzy entropy based on time-series signal, which can enhance the contrast and augment the accuracy of the detection of lesions in soft tissue. Fuzzy entropy assesses the irregularity of ultrasound time-series data through the detection of similarities within the signal waveform. Applying fuzzy entropy in ultrasound tissue characterization can provide more tissue information. The sliding windows are performed to traverse across the whole image with the step of one pixel. The signal in the sliding window calculates the fuzzy entropy (FE), and the FE value is assigned to the window’s center pixel. The entropy image will be constructed after obtaining all fuzzy entropies. The performance of the proposed FE imaging was compared with weighted Shannon entropy (WSE) imaging and B-mode imaging by simulations, ex vivo experiments, and clinical data. FE imaging got superior lesion area prediction with the AUC of 0.959 ± 0.042 (p < 0.0001), the accuracy of 99.88 ± 0.09 % (p < 0.0001), and the contrast-to-noise ratio of 7.806 ± 4.476 (p < 0.001) in the thyroid nodule diagnostic tests. The results show that FE imaging significantly increases the contrast of the images. Compared with WSE, the FE imaging method has better sensitivity to disorder degree and better recovery ability of tissue information when the amplitude of the signal decreases or the change of signal properties is small. FE imaging considers the characteristics of ultrasound time series. It has a better recovery ability for ultrasound signals, which is a promising technique for soft tissue diagnosis and thermal ablation monitoring.

SARIMA Model-Based Monte Carlo Simulation of Option Contract Design for Maize Seasonal Heavy Precipitation in Shenyang

Agricultural production is highly dependent on weather conditions such as temperature, light, water and heat. The huge loss caused by extreme disaster weather makes the global insurance and reinsurance market overwhelmed, and seriously affects the enthusiasm of agricultural investment and the development of agricultural economy. With unique topography and vast territory, China is vulnerable to complex and diverse climate disasters. In recent years, the annual average economic loss caused by extreme weather disasters has reached about 200-300 billion yuan, among which floods caused by extreme heavy rainfall are the main agricultural disasters in China. In order to enhance the "farmer - insurance company - government" interest association to resist the risk of flood disaster caused by extreme heavy rainfall, financial hedging derivatives of weather disaster risk have gradually become a new hedging tool besides traditional insurance and reinsurance. This paper takes Shenyang, the main grain producing area in northeast China, as the sample of the study area, and uses the SARIMA model to obtain the distribution characteristics of seasonal rainfall time series. Different seasonal rainfall index option products (call option and put option contracts) are designed respectively, and the final option pricing is obtained by Monte Carlo stochastic simulation. The seasonal rainfall call option and put option contracts have opened up a new hedging model of agricultural extreme weather catastrophe outside the insurance and reinsurance market, which has enriched the varieties of weather financial derivatives market in China, reduced the severe impact of extreme weather disasters on agriculture, and enhanced the ability of agricultural stakeholders to resist the risk of extreme weather disasters.

Predicting abrupt depletion of dissolved oxygen in Chaohu lake using CNN-BiLSTM with improved attention mechanism

Depletion of dissolved oxygen (DO) is a significant incentive for biological catastrophic events in freshwater lakes. Although predicting the DO concentrations in lakes with high-frequency real-time data to prevent hypoxic events is effective, few related experimental studies were made. In this study, a short-term predicting model was developed for DO concentrations in three problematic areas in China's Chaohu Lake. To predict the DO concentrations at these representative sites, which coincide with biological abnormal death areas, water quality indicators at the three sampling sites and hydrometeorological features were adopted as input variables. The monitoring data were collected every 4 h between 2020 and 2023 and applied separately to train and test the model at a ratio of 8:2. A new AC-BiLSTM coupling model of the convolution neural network (CNN) and the bidirectional long short-term memory (BiLSTM) with the attention mechanism (AM) was proposed to tackle characteristics of discontinuous dynamic change of DO concentrations in long time series. Compared with the BiLSTM and CNN-BiLSTM models, the AC-BiLSTM showed better performance in the evaluation criteria of MSE, MAE, and R2 and a stronger ability to capture global dependency relationships. Although the prediction accuracy of hypoxic events was slightly worse, the general time series characteristics of abrupt DO depletion were captured. Water temperature regularly affects DO concentrations due to its periodic variations. The high correlation and the universal importance of total nitrogen (TN) and total phosphorus (TP) with DO reveals that point source pollution are critical cause of DO depletion in the freshwater lake. The importance of NTU at the Zhong Miao Station indicates the self-purification capacity of the lake is affected by the flow rate changes brought by the tributaries. Calculating linear correlations of variables in conjunction with a permutation variable importance analysis enhanced the interpretability of the proposed model results. This study demonstrates that the AC-BiLSTM model can complete the task of short-term prediction of DO concentration of lakes and reveal its response features of timing and magnitude of abrupt DO depletion.

An approach for evaluating traffic safety of expressway weaving segments: Investigating risk patterns of lane-changing conflicts

Weaving segments, integral to expressway systems, face safety challenges due to frequent lane changes. While previous research has leveraged pre– and post–lane-changing (LC) data for risk estimation, few efforts have assessed time-series characteristics of LC conflict risks. This study proposes an approach to evaluate safety performance of weaving segments by investigating conflict risk patterns during the LC process using vehicle trajectory data. First, risk profiles are generated based on driving safety field theory for detected LC conflicts. These profiles are then categorized into distinct time-series patterns using k-shape clustering. Finally, determinants of these patterns are identified by enhanced random forest, which provides valuable insights into their relationship with risk severity. Analysis of trajectory data from 12 weaving segments in China reveals three main risk patterns (ascent, descent, and stabilization with a peak) for both rear-end and sideswipe conflicts. Ascending risk patterns signify more severe conflicts compared to descending ones. Increasing auxiliary lanes at weaving segments results in a more evenly distributed acceleration, potentially leading to a more severe LC conflict. The lowest risk is observed at the weaving segments with two auxiliary lanes. This study offers a novel perspective for mitigating LC crashes and enhancing traffic safety in weaving segments.

Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model

Effective air quality prediction models are crucial for the timely prevention and control of air pollution. However, previous models often fail to fully consider air quality’s temporal and spatial distribution characteristics. In this study, Xi’an City is used as the study area. Data from 1 January 2019 to 31 October 2020 are used as the training set, while data from 1 November 2020 to 31 December 2020 are used as the test set. This paper proposes a multi-time and multi-site air quality prediction model for Xi’an, leveraging a deep learning network model based on APSO-CNN-Bi-LSTM. The CNN model extracts the spatial features of the input data, the Bi-LSTM model extracts the time series features, and the PSO algorithm with adaptive inertia weight (APSO) optimizes the model’s hyperparameters. The results show that the model achieves the best results in terms of MAE and RMSE. Compared to the PSO-SVR, BPTT, CNN-LSTM, and GA-ACO-BP models, the MAE improved by 9.375%, 6.667%, 2.276%, and 4.975%, while the RMSE improved by 8.371%, 8.217%, 6.327%, and 5.293%. These significant improvements highlight the model’s accuracy and its promising application prospects.

Electric theft detection based on multi-source heterogeneous time series GRU network

Abstract Electricity theft has long been one of the major problems faced by power supply enterprises. To improve the robustness and accuracy of power theft detection, this article explores the method of multi-source heterogeneous time series feature fusion and designs a gated cyclic unit network model that adapts to its feature fusion. Firstly, through correlation analysis, it is verified that there is a logical correlation between different time series features and classification features, providing a theoretical basis for feature fusion. Then, an encoder decoder model framework is constructed with an attention mechanism to achieve effective fusion and state detection of user multi-source time series features. The experimental results show that compared to a single data source, the fusion of multi-source features can significantly improve detection performance, and the designed model is superior to the control model. This study provides a reference for constructing an efficient power theft detection system and also provides examples of multi-source heterogeneous feature fusion in related fields.

Rethinking general time series analysis from a frequency domain perspective

Recently, Transformers and MLPs based models have dominated and made significant progress in time series analysis. However, these methods struggle to capture the complete periodic features to model the global dependencies of time series. To this end, we provide a new perspective to explore the potential of time series in the frequency domain. Specifically, we propose the DFT-based Amplitude-Phase Decoupling Network (APDNet), which learns the significance changes and periodicity characteristics of time series by decoupling the real and imaginary parts in the complex frequency domain. First, we propose the Fourier Temporal/Variable Interaction Module to model time dependency and dependency multiple variables, respectively. Secondly, we innovatively propose the Dimensional Expanded Feature Embedding Module (DEFEM), which expands time series into a higher-dimensional representation space to preserve more independent features. Finally, through extensive experiments on multiple real-world datasets, our APDNet achieves state-of-the-art performance in various tasks such as long-term and short-term forecasting, anomaly detection, imputation, and classification. Code is available at: https://github.com/CodeJester196/APDNet.

Enhance carbon emission prediction using bidirectional long short-term memory model based on text-based and data-driven multimodal information fusion

Accurate projections of carbon emissions are essential for climate change responses and environmental policy development. However, the complexity of carbon emission projections has increased due to various factors like technology and policy, posing challenges for accuracy. Many studies rely on time-series data alone, overlooking the combined impact of multiple information sources on carbon emissions. To overcome these bottlenecks, a hybrid model is proposed, integrating multimodal data, convolutional neural network inceptionv2 (CNN-inceptionv2), text convolutional neural network (TextCNN) optimized by attention mechanism, and bi-directional long and short-term memory (BiLSTM). Firstly, the time series feature set and text feature set are extracted using the CNN-inceptionv2 and the TextCNN, respectively. Subsequently, the feature sets of the two modal information are fused and outputted into a multimodal feature set, which are ultimately predicted using BiLSTM. The validity of the study model was also verified by different methods such as ablation experiments, single model and combined model. The coefficient of determination (R2) values were 0.986 for the validation set and 0.933 for the prediction set. In comparison to other models, the present model showed an average improvement of 16.6% in R2 values, fully indicating its superior predictive performance. The high point and interval predictions of carbon emissions further verified the superior prediction accuracy of the present model. The present model can accurately predict carbon emissions, providing data references for carbon emission decision makers. Additionally, it further enriches the methodological system of carbon emission prediction and aids in strategic planning for future carbon emission and energy management.

In-service Load Monitoring for an UAV Digital Twin

A load monitoring system plays an important role in recovering the actual load spectra of aeronautical structures, contributing to the online evolution of airframe digital twins. In scenarios where many aircrafts lack on-board strain sensors during the service phase, yet some strain data is available during the test flight phase, our innovative approach utilises deep learning-based flight-strain prediction and an inverse-direct approach for in-service load monitoring. Initially, a deep learning approach is employed during the test flight prior to service to establish a flight parameter-strain prediction model. This model, incorporating time series features of flight and strain da-ta, exhibits superior predictive accuracy compared to traditional regression methods. Moving into the subsequent service phase, the flight parameter-strain prediction model seamlessly integrates with an inverse-direct load monitoring method. This integrated approach facilitates real-time monitoring of full-field load distribution, relying solely on flight parameters. Validation of the approach utilises flight test data from an unmanned aerial vehicle, revealing better performance compared with the strain-measurement-based method. Notably, our method's efficacy extends across diverse aircraft types, as it does not rely on on-board strain sensors during the service phase.

거시경제 및 인구특성의 변화가 수도권 및 비수도권 주택 매매시장에 미치는 영향에 관한 연구

This study attempted to compare and derive the impact on the housing sales market in the metropolitan area and local cities and to present implications in terms of the real estate brokerage market. The Vector Error Correction Model was used as the analysis method in consideration of the time series characteristics and cointegration relationship of the used macroeconomic and demographic variables. The analysis results and implications are summarized as follows. In the case of the metropolitan area model, interest rates, consumer price indices, population growth rates, and fertility rates were the main factors influencing the housing sales market. The five metropolitan city models are the main factors influencing interest rates, economic growth rates, and consumer price indices, while the explanatory power of the fertility rate is very low. Considering these results, housing can be classified as an investment product in the metropolitan area, and local cities can be classified as a housing sales market with strong characteristics as a consumer product. Long-term housing sales are not expected to increase significantly due to the recent decline in investment and real demand due to the stabilization of housing prices due to high-interest rates. The housing brokerage market needs to be developed into a convergence field by combining highly relevant management and consulting.

Analyzing the Dynamics of Customer Behavior: A New Perspective on Personalized Marketing through Counterfactual Analysis

The existing body of research on dynamic customer segmentation has primarily focused on segment-level customer purchasing behavior (CPB) analysis to tailor marketing strategies for distinct customer groups. However, these approaches often lack the granularity required for personalized marketing at the individual level. Moreover, the analysis of customer transitions between different groups has largely been overlooked. This study addresses these gaps by developing an efficient framework that enables businesses to forecast customer behavior, assess the impact of various strategies on each customer separately, and analyze customer transition between segments. This can facilitate providing personalized marketing strategies, fostering a gradual transition toward a desired customer status, and enhancing the overall marketing precision. In this study, we employ time series feature vectors encompassing recency, frequency, monetary value, and lifespan, applying the K-means algorithm with a range of distance metrics for customer segmentation along with classification algorithms to predict customer behavior. Leveraging counterfactual analysis, we establish a solution for analyzing customer transitions between groups and evaluating personalized marketing strategies. Our findings underscore the superior performance of the Euclidean distance metric, closely followed by the Manhattan distance, in distinguishing the patterns in time series customer behavior, with logistic regression excelling in predicting customer status. This study enables decision-makers to forecast the impact of diverse marketing strategies on customer behavior which facilitates customer retention and engagement through well-informed decisions.

Timeseries Feature Extraction for Dataset Creation in Prognostic Health Management

Timeseries Feature Extraction for Dataset Creation in Prognostic Health Management