Dynamic spatiotemporal correlation coefficient based on adaptive weight

Risk management is an important aspect of financial research because correlations among financial data are essential in evaluating portfolio risk.Among various correlations,spatiotemporal correlations involve economic entity attributes and are interrelated in space and time.Such correlations have therefore drawn increasing attention in financial risk management.However,classical correlation measurements are typically based on either time series correlations or spatial dependence;they cannot be directly applied to financial data with spatiotemporal correlations.The spatiotemporal correlation coefficient model with adaptive weight proposed in this paper can(1)address the absolute quantity,dynamic quantity,and dynamic development of financial data and(2)be used for risk grading,financial risk evaluation,and portfolio management.To verify the validity and superiority of this model,cluster analysis results and portfolio performance are compared with a classical model with time series correlation or spatial correlation,respectively.Empirical findings show that the proposed coefficient is highly effective and convenient compared to others.Overall,our method provides a highly efficient financial risk management method with valuable implications for investors and financial institutions.

Sensory Data Prediction Using Spatiotemporal Correlation and LSTM Recurrent Neural Network

The Wireless Sensor Networks(WSNs)are widely utilized in various industrial and environmental monitoring applications.The process of data gathering within the WSN is significant in terms of reporting the environmental data.However,it might occur that certain sensor node malfunctions due to the energy draining out or unexpected damage.Therefore,the collected data may become inaccurate or incomplete.Focusing on the spatiotemporal correlation among sensor nodes,this paper proposes a novel algorithm to predict the value of the missing or inaccurate data and predict the future data in replacement of certain nonfunctional sensor nodes.The Long-Short-Term-Memory Recurrent Neural Network(LSTM RNN)helps to more accurately derive the time-series data corresponding to the sets of past collected data,making the prediction results more reliable.It is observed from the simulation results that the proposed algorithm provides an outstanding data gathering efficiency while ensuring the data accuracy.

Data-driven unsupervised anomaly detection and recovery of unmanned aerial vehicle flight data based on spatiotemporal correlation

Anomaly detection is crucial to the flight safety and maintenance of unmanned aerial vehicles(UAVs)and has attracted extensive attention from scholars.Knowledge-based approaches rely on prior knowledge,while model-based approaches are challenging for constructing accurate and complex physical models of unmanned aerial systems(UASs).Although data-driven methods do not require extensive prior knowledge and accurate physical UAS models,they often lack parameter selection and are limited by the cost of labeling anomalous data.Furthermore,flight data with random noise pose a significant challenge for anomaly detection.This work proposes a spatiotemporal correlation based on long short-term memory and autoencoder(STCLSTM-AE)neural network data-driven method for unsupervised anomaly detection and recovery of UAV flight data.First,UAV flight data are preprocessed by combining the Savitzky-Golay filter data processing technique to mitigate the effect of noise in the original historical flight data on the model.Correlation-based feature subset selection is subsequently performed to reduce the reliance on expert knowledge.Then,the extracted features are used as the input of the designed LSTM-AE model to achieve the anomaly detection and recovery of UAV flight data in an unsupervised manner.Finally,the method's effectiveness is validated on real UAV flight data.

Sound speed profiles in high spatiotemporal resolution using multigrid three-dimensional variational method:a coastal experiment off northern Shandong Peninsula

It is essential to ac quire sound speed profiles(SSPs)in high-precision spatiotemporal resolution for undersea acoustic activities.However,conventional observation methods cannot obtain high-resolution SSPs.Besides,S SPs are complex and changeable in time and space,especially in coastal areas.We proposed a new space-time multigrid three-dimensional variational method with weak constraint term(referred to as STC-MG3DVar)to construct high-precision spatiotemporal resolution SSPs in coastal areas,in which sound velocity is defined as the analytical variable,and the Chen-Millero sound velocity empirical formula is introduced as a weak constraint term into the cost function of the STC-MG3DVar.The spatiotemporal correlation of sound velocity observations is taken into account in the STC-MG3DVar method,and the multi-scale information of sound velocity observations from long waves to short waves can be successively extracted.The weak constraint term can optimize sound velocity by the physical relationship between sound velocity and temperature-salinity to obtain more reasonable and accurate SSPs.To verify the accuracy of the STC-MG3DVar,SSPs observations and CTD observations(temperature observations,salinity observations)are obtained from field experiments in the northern coastal area of the Shandong Peninsula.The average root mean square error(RMSE)of the STC-MG3DVar-constructed SSPs is 0.132 m/s,and the STC-MG3DVar method can improve the SSPs construction accuracy over the space-time multigrid 3DVar without weak constraint term(ST-MG3DVar)by 10.14%and over the spatial multigrid 3DVar with weak constraint term(SC-MG3DVar)by 44.19%.With the advantage of the constraint term and the spatiotemporal correlation information,the proposed STC-MG3DVar method works better than the ST-MG3DVar and the SCMG3DVar in constructing high-precision spatiotemporal re solution SSPs.

Differential Privacy Trajectory Protection Method Based on Spatiotemporal Correlation

Location-based services provide service and convenience,while causing the leakage of track privacy.The existing trajectory privacy protection methods lack the consideration of the correlation between the noise sequence,the user’s original trajectory sequence,and the published trajectory sequence.And they are susceptible to noise filtering attacks using filtering methods.In view of this problem,a differential privacy trajectory protection method based on spatiotemporal correlation is proposed in this paper.With this method,the concept of correlation function was introduced to establish the correlation constraint of release track sequence,and the least square method was used to fit the user’s original track and the overall direction of noise sequence to construct noise candidate set.It ensured that the added noise sequence has spatiotemporal correlation with the user’s original track sequence and release track sequence.Also,it effectively resists attackers’denoising attacks,and reduces the risk of trajectory privacy leakage.Finally,comparative experiments were carried out on the real data sets.The experimental results show that this method effectively improves the privacy protection effect and the data availability of the release track,and it also has better practicability.

Effects of meteorological factors on different grades of winter wheat growth in the Huang-Huai-Hai Plain, China

The sown area of winter wheat in the Huang-Huai-Hai（HHH） Plain accounts for over 65% of the total sown area of winter wheat in China. Thus, it is important to monitor the winter wheat growth condition and reveal the main factors that influence its dynamics. This study assessed the winter wheat growth condition based on remote sensing data, and investigated the correlations between different grades of winter wheat growth and major meteorological factors corresponding. First, winter wheat growth condition from sowing until maturity stage during 2011–2012 were assessed based on moderate-resolution imaging spectroradiometer（MODIS） normalized difference vegetation index（NDVI） time-series dataset. Next, correlation analysis and geographical information system（GIS） spatial analysis methods were used to analyze the lag correlations between different grades of winter wheat growth in each phenophase and the meteorological factors that corresponded to the phenophases. The results showed that the winter wheat growth conditions varied over time and space in the study area. Irrespective of the grades of winter wheat growth, the correlation coefficients between the winter wheat growth condition and the cumulative precipitation were higher than zero lag（synchronous precipitation） and one lag（pre-phenophase precipitation） based on the average values of seven phenophases. This showed that the cumulative precipitation during the entire growing season had a greater effect on winter wheat growth than the synchronous precipitation and the pre-phenophase precipitation. The effects of temperature on winter wheat growth varied according to different grades of winter wheat growth based on the average values of seven phenophases. Winter wheat with a better-than-average growth condition had a stronger correlation with synchronous temperature, winter wheat with a normal growth condition had a stronger correlation with the cumulative temperature, and winter wheat with a worse-than-average growth condition had a stronger correlation with the pre-phenophase temperature. This study may facilitate a better understanding of the quantitative correlations between different grades of crop growth and meteorological factors, and the adjustment of field management measures to ensure a high crop yield.

Attention-based spatio-temporal graph convolutional network considering external factors for multi-step traffic flow prediction

Traffic flow prediction is an important part of the intelligent transportation system. Accurate multi-step traffic flow prediction plays an important role in improving the operational efficiency of the traffic network. Since traffic flow data has complex spatio-temporal correlation and non-linearity, existing prediction methods are mainly accomplished through a combination of a Graph Convolutional Network (GCN) and a recurrent neural network. The combination strategy has an excellent performance in traffic prediction tasks. However, multi-step prediction error accumulates with the predicted step size. Some scholars use multiple sampling sequences to achieve more accurate prediction results. But it requires high hardware conditions and multiplied training time. Considering the spatiotemporal correlation of traffic flow and influence of external factors, we propose an Attention Based Spatio-Temporal Graph Convolutional Network considering External Factors (ABSTGCN-EF) for multi-step traffic flow prediction. This model models the traffic flow as diffusion on a digraph and extracts the spatial characteristics of traffic flow through GCN. We add meaningful time-slots attention to the encoder-decoder to form an Attention Encoder Network (AEN) to handle temporal correlation. The attention vector is used as a competitive choice to draw the correlation between predicted states and historical states. We considered the impact of three external factors (daytime, weekdays, and traffic accident markers) on the traffic flow prediction tasks. Experiments on two public data sets show that it makes sense to consider external factors. The prediction performance of our ABSTGCN-EF model achieves 7.2%–8.7% higher than the state-of-the-art baselines.

Flexible optoelectronic neural transistors with broadband spectrum sensing and instant electrical processing for multimodal neuromorphic computing

A flexible optoelectronic neural transistor(OENT)that consists of a one‐step spin‐coated tri‐blend film composed of 2,7‐dioctyl[1]benzothieno[3,2‐b][1]benzothiophene(C8‐BTBT),poly(3‐hexylthiophene‐2,5‐diyl)(P3HT),and poly(methyl methacrylate)(PMMA)is demonstrated.The C8‐BTBT and P3HT phases in the film partially segregate into distinct domains,which combine to provide broadband spectrum sensing,and instant electrical‐processing capabilities dominated by C8‐BTBT.The OENT is sensitive to solar radiation from the near‐ultraviolet(NUV)and to visible(Vis)radiation from blue to red.When exposed to NUV radiation,the OENT responds sensitively and retains the memory of the exposure for over 10^(3 )s.The OENT provides a warning of excessive chronic exposure to harmful NUV.These properties allow high‐pass filtering with different cut‐off frequencies fc that can restrict the reception of blue,green,or red.These switchable fc enables sensitive image reconstruction and multitarget monitoring.The device combined with a chitosan gel achieves strictly defined short‐range plasticity of<1 s that can achieve diverse instant‐computing applications such as spatiotemporally correlated coding and logic functions.Stable real‐time signal processing facilitates the realization of a Morse‐code recognition system constructed using neuro‐morphological hardware,achieving highly accurate character recognition.This study provides a useful resource that can have applications in wearable biomedical electronics and multimodal neuromorphic computing.