Characteristics Of Time Series Research Articles

Ensemble empirical mode decomposition based deep learning models for forecasting river flow time series

River flow forecasting is important for flood prediction and effective utilization of water resources. This study proposed a comprehensive methodology that simultaneously enables the creation of multiple datasets and a comparison of multiple learning models for forecasting river flows. First, the data characteristics of the river flow time series were assessed based on its nature and pattern characteristics. Second, data decomposition was made for simplifying the complex data into decomposed modes. Ensemble Empirical Mode Decomposition (EEMD) was used for this purpose that provides intrinsic mode functions (IMFs). In addition, as benchmark, traditional time series decomposition was also applied over the complex data. Third, based on their data characteristics, the IMFs were combined into different components. These components along with the traditional time series decomposition create multiple datasets. Then, the components from the different datasets were forecasted using deep learning models, mainly BPNN (Back-Propagation Neural Network), CNN (Convoluted Neural Network) and LSTM (Long-Short Term Memory). Then, ensemble prediction was used to get the final output. For verification and benchmark purpose, we have also used statistical models like SARIMA over the datasets. A comprehensive evaluation and model comparisons were made using Diebold-Mariano (DM) test, k-fold cross validation and with the results of Prophet. In addition, the temporal convolution network (TCN) and the gated recurrent unit (GRU) were also modelled. Finally, all the DL modelling was performed for two data situations: with missing data deleted and missing data imputed. An empirical case study was done considering the average of daily time series data of Neeleswaram Hydrological Observation (HO) station over the Periyar river of South India for the period 2017–2021. The empirical assessment was made using several tests for all the different datasets and different learning models for further insights. Our results indicate that the deep learning-based models offer better predictions for complex time series in comparison to traditional statistical models. The DL models applied on the original time series data provided robust predictions with good performance such as low RMSE and high correlation values. However, the DL models’ performances are not enhanced through integration with time series decomposition and EEMD. Furthermore, the empirical results and the k-fold cross validation-based test indicate that all the DL models show equivalent performances. Interestingly, the DL models show superior performance over Prophet.

Analysis of multi-factors affecting resource consumption efficiency in Vietnam

Theprimary objective of this study is todelve into the factors potentially influencing resource utilisationefficiency at the provincial level in Vietnam. We leverage 185 data points gathered from 63 Vietnamese cities or provinces, obtained from two key sources: the General Statistical Office of Vietnam and the Ministry of Information and Communications Vietnam. The collected data was subjected to analysis using an empirical econometric model that incorporates time-series characteristics to explore the connections between these potential determinants of resource consumption efficiency. The analysis revealed a strong correlation between resource utilisationefficiency and the quality of digital transformation, logistics performance, and various aspects of public governance,including accountability to higher authorities, control of corruption, administrative procedures, and public service delivery. Additionally, the provincial competitiveness index, encompassing factors like entry costs, policy bias, and labourpolicy, also demonstrated a significant association. These findings highlight a clear link between effective public governance and efficient resource utilisationin Vietnamese provinces. This suggests that robust public governance systems, combined with advancements in digitalisationand logistics, can play a crucial role in optimisingresource utilisationwithin Vietnamese provinces.

An improved transformer‐based model for long‐term 4D trajectory prediction in civil aviation

AbstractFour‐dimensional trajectory prediction is a crucial component of air traffic management, and its accuracy is closely related to the efficiency and safety of air transportation. Although long short‐term memory (LSTM) or its variants have been widely used in recent studies, they may produce unacceptable results in long‐term prediction due to the iterative output that accumulates error. To address this issue, a transformer‐based long‐term trajectory prediction model is proposed here, which utilizes the self‐attention mechanism to extract time series features from historical trajectory data. For long‐term prediction scenarios, we a trajectory stabilization module is introduced to ensure the stationarity of the time series for better predictability. Additionally, the transformer output strategy is improved to generate the prediction sequence by a single step instead of serial dynamic decoding, thus effectively enhancing the precision and inference speed. The proposed model is validated using real data obtained from China's Southwest Air Traffic Management Bureau. The experimental results demonstrate that this model outperforms the benchmark model. Further ablation experiments and visualizations are performed to analyze the impact of trajectory stabilization and one‐step inference strategy.

A bi-level mode decomposition framework for multi-step wind power forecasting using deep neural network

The proportion of wind energy in global energy structure is growing rapidly, promoting the development of wind power forecasting (WPF) technologies to solve the uncertainty and intermittence of wind power generation. However, the nonlinear and stochastic features of wind power time series restrain the accuracy of multi-step prediction performance. A multi-step WPF (MS-WPF) approach based on a time series bi-level empirical mode decomposition (BLEMD) method and BiLSTM neural network is proposed in this paper to improve the WPF accuracy of regional wind power generators. Since the uncertainty is always generated through coupled factors from both wind and weather-to-power conversion, the linearity feature is first introduced as an aspect apart from the frequency in the proposed approach to decompose the wind power time sequence data. The proposed BLEMD introduces Pearson product-moment correlation coefficient to evaluate the linearity of time series and a linearity-based decomposition algorithm is designed accordingly. To further enhance the precision and release computation burdens, a DL-based prediction strategy, including a BiLSTM network, a CNN-BiLSTM network, and a mean weight estimation method are implemented to predict the components separately. The proposed method only relies on local data, greatly reducing the data acquisition and computation cost. The precision of the proposed MS-WPF is verified by a 2.5 kW wind turbine with horizons from 5 s to 30 s, a 1.5 MW wind turbine with horizons from 10 min to 1 h, and a 51 MW wind farm with horizons from 1 h to 6 h. The comparative experimental results with other cutting-edge methods indicated that the proposed MS-WPF has superior prediction accuracy and stable performance for multi-step prediction.

STFT-TCAN: A TCN-attention based multivariate time series anomaly detection architecture with time-frequency analysis for cyber-industrial systems

Networks and industrial systems play a pivotal role in modern society, and their security has garnered increasing attention. Anomalies within industrial equipment may propagate through fault transmission, leading to a cascade of failures. Additionally, cyberattacks on equipment can result in significant losses. Therefore, in the realm of industrial and cyberspace domains, an effective multivariate time series anomaly detection system for monitoring equipment is instrumental in ensuring the healthy operation of the machinery. Nevertheless, detecting anomalies in numerous time series remains challenging, stemming from the absence of anomaly labels and the complexity of the data patterns. Existing algorithms predominantly concentrate on modeling within the time domain, falling short in fully leveraging the informative features present in frequency domain data, resulting in diminished detection performance. This paper introduces STFT-TCAN, a model for anomaly detection in time series that seamlessly integrates information from both time and frequency domains for extracting data features. Sliding windows and the Short Time Fourier Transform (STFT) are utilized to construct a frequency matrix, effectively amalgamating the characteristics of both time and frequency domains within the time series. Furthermore, the model employs Temporal Convolutional Networks (TCN) and Transformer attention mechanisms (which combined to form the TCAN module) to capture the features of multivariate time series, thereby resulting in heightened detection accuracy. The proposed model undergoes validation on six publicly available datasets, showcasing the superior performance of the STFT-TCAN model in comparison to current baseline methods. It adeptly extracts features from both frequency and time domains in sequential data, thereby achieving state-of-the-art performance in tasks related to anomaly detection in multivariate time series.

MicroIRC: Instance-level Root Cause Localization for Microservice Systems

The use of microservice architecture is gaining popularity in the development of web applications. However, identifying the root cause of a failure can be challenging due to the complexity of interconnected microservices, long service invocation links, dynamic changes in service states, and the abundance of service deployment nodes. Furthermore, as each microservice may have multiple instances, it can be difficult to identify instance-level failures promptly and effectively when the microservice topology and failure types change dynamically. To address this issue, we propose MicroIRC (Instance-level Root Cause Localization for Microservice Systems), a novel metrics-based approach that localizes root causes at the instance level while exhibiting robustness to adapt to dynamic changes in topology and new types of anomalies. We begin by training a graph neural network to fit different root cause types based on extracted time series features of microservice system metrics. Next, we construct a heterogeneous weighted topology (HWT) of microservice systems and execute a personalized random walk to identify root cause candidates. These candidates, along with real-time metrics from the anomalous time window, are then fed into the trained graph neural network to generate a ranked root cause list. Experiments conducted on five real-world datasets demonstrate that MicroIRC can accurately locate the root cause of microservices at the instance level, achieving a precision rate of 93.1% for the top five results. Furthermore, compared to the state-of-the-art methods, MicroIRC can improve the accuracy of root cause localization by more than 17% at the service level and more than 11.5% at the instance level. Remarkably, it exhibits robustness in scenarios involving new failure types, achieving an accuracy of 84.2% for the top result amid dynamic topological changes.

Solar Power Generation Forecast Using Multivariate Convolution Gated Recurrent Unit Network

For the advancement of smart grids, solar power generation predictions have become an important research topic. In the case of using traditional modeling methods, excessive computational costs may be incurred and it is difficult for these methods to learn the multi-variable dependencies of the data. Therefore, in this paper, a deep learning model was used to combine convolutional neural networks and long short-term memory recurrent network predictions. This method enables hourly power generation one day into the future. Convolutional neural networks are used to extract the features of multiple time series, while long short-term memory neural networks predict multivariate outcomes simultaneously. In order to obtain more accurate prediction results, we performed feature selection on meteorological features and combined the selected weather features to train the prediction model. We further distinguished sunny- and rainy-day models according to the predicted daily rainfall conditions. In the experiment, it was shown that the method of combining meteorological features further reduced the error. Finally, taking into account the differences in climate conditions between the northern and southern regions of Taiwan, the experimental results of case studies involving multiple regions were evaluated to verify the proposed method. The results showed that training combined with selected meteorological features can be widely used in regions with different climates in Taiwan.

Video-Based Sign Language Recognition via ResNet and LSTM Network.

Sign language recognition technology can help people with hearing impairments to communicate with non-hearing-impaired people. At present, with the rapid development of society, deep learning also provides certain technical support for sign language recognition work. In sign language recognition tasks, traditional convolutional neural networks used to extract spatio-temporal features from sign language videos suffer from insufficient feature extraction, resulting in low recognition rates. Nevertheless, a large number of video-based sign language datasets require a significant amount of computing resources for training while ensuring the generalization of the network, which poses a challenge for recognition. In this paper, we present a video-based sign language recognition method based on Residual Network (ResNet) and Long Short-Term Memory (LSTM). As the number of network layers increases, the ResNet network can effectively solve the granularity explosion problem and obtain better time series features. We use the ResNet convolutional network as the backbone model. LSTM utilizes the concept of gates to control unit states and update the output feature values of sequences. ResNet extracts the sign language features. Then, the learned feature space is used as the input of the LSTM network to obtain long sequence features. It can effectively extract the spatio-temporal features in sign language videos and improve the recognition rate of sign language actions. An extensive experimental evaluation demonstrates the effectiveness and superior performance of the proposed method, with an accuracy of 85.26%, F1-score of 84.98%, and precision of 87.77% on Argentine Sign Language (LSA64).

Time series analysis of PM2.5 pollution risk based on the supply and demand of PM2.5 removal service: a case study of the urban areas of Beijing.

Demonstrating the temporal changes in PM2.5 pollution risk in regions facing serious PM2.5 pollution problems can provide scientific evidence for the air pollution control of the region. However, research on the variation of PM2.5 pollution risk on a fine temporal scale is very limited. Therefore, we developed a method for quantitative characterizing PM2.5 pollution risk based on the supply and demand of PM2.5 removal services, analyzed the time series characteristics of PM2.5 pollution risk, and explored the reasons for the temporal changes using the urban areas of Beijing as the case study area. The results show that the PM2.5 pollution risk in the urban areas of Beijing was close between 2008 and 2012, decreased by approximately 16.3% in 2016 compared to 2012, and further decreased by approximately 13.2% in 2021 compared to 2016. The temporal variation pattern of the PM2.5 pollution risk in 2016 and 2021 showed significant differences, including an increase in the number of risk-free days, a decrease in the number of heavily polluted days, and an increase in the stability of the risk day sequence. The significant reduction in risk level was mainly attributed to Beijing's air pollution control measures, supplemented by the impact of COVID-19 control measures in 2021. The results of PM2.5 pollution risk decomposition indicate that compared to the previous 2years, the stability and predictability of the risk variation in 2016 increased, but the overall characteristics of high risk from November to February and low risk from April to September did not change. The high risk from November to February was mainly due to the demand for coal heating during this period, a decrease in PM2.5 removal service supply caused by plant leaf fall, and the common occurrence of temperature inversions in winter, which hinders the diffusion of air pollutants. This study provides a method for the analysis of PM2.5 pollution risk on fine temporal scales and may provide a reference for the PM2.5 pollution control in the urban areas of Beijing.

Adjusting rainfall inputs to hydrological models in a data-scarce area of Southern Africa

ABSTRACT A recent study in headwater sub-basins of the Zambezi River concluded that a machine learning model (HydroForecast) outperformed a deterministic monthly time-step water balance model (Pitman) when observed streamflow data were used as part of the training data for the machine learning approach. This was partly attributed to non-stationary bias in the input rainfall data that has a greater impact on a model that has mass balance constraints. The current study investigated methods of adjusting the input rainfall data based on an analysis of the streamflow simulation errors and found that the results for the water balance model could be improved so that they compared more favourably with the machine learning model. The improvements were achieved without changing the long-term statistical characteristics of the original monthly rainfall time series. It is acknowledged that there is some evidence that differences in daily rainfall patterns might explain some of the simulation errors.

Effects of deep coal mining on groundwater hydrodynamic and hydrochemical processes in a multi-aquifer system: Insights from a long-term study of mining areas in ecologically fragile western China

The groundwater hydrodynamic and hydrochemical process of the multi-aquifer system will experience complicated and serious influence under deep coal mining disturbance. There is relatively little research that has integrated hydrodynamic and hydrochemical properties of groundwater to investigate the spatiotemporal distribution characteristics and evolution patterns of hydrogeochemistry and hydrodynamic information in deep multi-aquifer systems. The study of the groundwater hydrodynamic and hydrochemical spatiotemporal coupling response of multi-aquifer systems under the deep and special thick coal seam mining-motivated effect in ecologically fragile western mining areas is of great significance for the safe mining of coal resources and ecological environment protection. In this research, the hydrochemical analysis data composed of 218 groundwater samples from Tangjiahui coalfield, Northwest China with 1526 measurements and a 6-year (2016–2021) sampling period were collected for studying the hydrogeochemical spatiotemporal evolution process and governing mechanism of the multi-aquifer system using hierarchical cluster analysis, ion-ratio method, saturation index and multidimensional statistical analysis. Additionally, wavelet analysis and cross-wavelet coherence analysis were implemented to quantitatively recognize the spatiotemporal variation characteristics of hydrodynamic information and analyze the coherence relationships between time series. The results demonstrate that the hydrochemical characteristics exhibit significant spatial differences, while the temporal variation of hydrochemical characteristics in the Permian Shanxi Formation fractured sandstone aquifer (PSFFA), mine water (MW), and Ordovician karst limestone aquifer (OKA) is not significant. The water-rock interaction is the predominant control mechanism for the spatial evolution of hydrogeochemistry in the research area. Moreover, the large-scale mining of deep coal seams controls the type and degree of water-rock interactions by damaging the structure of aquifers and altering the hydrodynamic conditions of groundwater. The period from 2016 to 2021 exhibits multi-time scale characteristics in time series of precipitation, mine water discharge, and the water level of PSFFA and OKA. The mine water discharge has a positive correlation with the water level of PSFFA and OKA, whereas the significant period of precipitation and the water level of PSFFA coherence is not obvious. The research findings not only provide in-depth insights to protect the groundwater resources in water-shortage mining areas but also promote the secure mining of deep coal resources.

Detection of emerald ash borer damage using an improved change detection method: Integrating host phenology and pest life history

Invasive Emerald Ash Borer (EAB) damage pose significant challenges for sustainable forest management, necessitating accurate mapping of damaged ash trees. Traditional change detection methods, using time-series imagery, are essential for monitoring forest disturbances but complicated by abnormal fluctuations in original time-series features. Tree phenology also complicates this process by masking the reflectance characteristics indicative of EAB infestation. To address these challenges, we propose an improved change detection method integrating patterns from host tree phenology and EAB life history. This improved method includes: (1) select the indices with time stability to enhance detection reliability by partial least squares method (PLS); (2) correction on negative change values before and positive change values after the phenological peak based on known patterns of tree phenology and EAB life history. Result confirms that this method effectively reflects the seasonal growth and decline dynamics of ash trees, revealing the impacts of phenology and EAB infestation. EAB-damaged trees exhibited slower growth in May and premature decline in July compared with healthy tree, with the damage severity influencing the rate of leaf decline. This proposed method achieved an overall accuracy of 53.4%-76.7% across different months for ash trees with health, light and severe damage. This study highlights the capabilities of integrating pest life history and phenology in change detection method and provide a new method to monitor individual tree health across large areas by high-resolution satellite imagery.

Prediction of photovoltaic power generation based on a hybrid model

In order to fully exploit the relationship between temporal features in photovoltaic power generation data and improve the prediction accuracy of photovoltaic power generation, a photovoltaic power generation forecasting method is proposed based on a hybrid model of the convolutional neural network (CNN) and extreme gradient boost (XGBoost). Taking the historical data of China’s photovoltaic power plants as a sample, the high-dimensional mapping relationship of photovoltaic power generation variables is extracted based on the convolutional layer and pooling layer of the CNN network to construct a high-dimensional time-series feature vector, which is an input for the XGBoost. A photovoltaic power generation prediction model is established based on CNN-XGBoost by training CNN and XGBoost parameters. Since it is difficult for a single model to achieve optimal prediction accuracy under different weather conditions, the k-means clustering algorithm is used to group the power datasets and train independent models to improve prediction accuracy. Through the actual data verification of photovoltaic power plants, the proposed photovoltaic power generation prediction model can accurately predict the power, which shows high prediction accuracy and generalization ability compared with other methods.

Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making.

Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.

Multitimescale Template Matching: Discovering Eruption Precursors across Diverse Volcanic Settings

Abstract Volcanic eruptions pose significant risks, demanding precise monitoring for timely hazard mitigation. However, interpreting noisy seismic data for eruptive precursors remains challenging. This study introduces a novel methodology that extends an earlier time-series feature engineering approach to include template matching against prior eruptions. We aim to identify subtle signals within seismic data to enhance our understanding of volcanic activity and future hazards. To do this, we analyze the continuous seismic record at a volcano and identify the time-series elements that regularly precede eruptions and the timescales over which these are observable. We conduct tests across various time lengths, ranging from 1 to 60 days. For Copahue (Chile/Argentina), Pavlof (Alaska), Bezymianny (Russia), and Whakaari (New Zealand) volcanoes, we confirm statistically significant eruption precursors. In particular, a feature named change quantiles (0.2–0.8), which is related to the conditional dynamics of surface acceleration at the volcano, emerges as a key indicator of future eruptions over 14-day timescales. This research offers new methods for real-time seismovolcanic monitoring, minimizing the effects of unknown, spurious noise, and discerning recurrent patterns through template matching. By providing deeper insights into pre-eruptive behavior, it may lead to more effective hazard reduction strategies, enhancing public safety around active volcanoes.

A common feature-driven prediction model for multivariate time series data

Multivariate time series data contain a variety of common features that are difficult to extract, among which the sudden irregular fluctuation trend, the trend feature of large fluctuation amplitude, and the long-term dependency relationship in the time series have an important impact on the accuracy of the prediction model. To predict non-stationary trends in large-scale data accurately using the common characteristics of multivariate time series. A novel generalised forecasting model NeA3L based on common features of time series is developed. The NeA3L model utilizes multiple independent parallel feature extraction modules to obtain the common features of multivariate time series. It utilizes the three-layer iterative structure to deal with sudden irregular fluctuation patterns. NeA3L optimizes the network structure to realize the heterogeneous codec structure. It applies the attention mechanism between the encoder and the decoder to accomplish the multivariate prediction of the multivariate time series, which has good stability for predicting various types of multivariate data. A comparison of the NeA3L model with nine current time series prediction methods on four publicly available datasets shows that the NeA3L model outperforms the current methods in various evaluation metrics. The RMSE is improved by 2.3 %, 26.5 %, 28.9 %, and 20.84 % on average, respectively. The NeA3L model can be universally applicable to the field of multivariate time series prediction, which is important for optimizing the intelligent management and decision-making.

TodyNet: Temporal dynamic graph neural network for multivariate time series classification

Multivariate time series classification (MTSC) is a crucial data mining task that can be effectively tackled using prevalent deep learning technology. However, current methods often overlook hidden dependencies across dimensions and struggle to capture dynamic time series features adequately, leading to poor accuracy. To tackle this challenge, we propose TodyNet, a novel dynamic temporal graph neural network. TodyNet extracts latent spatio-temporal dependencies without predefined graph structures, facilitating information flow among isolated but implicitly interdependent variables. It captures associations between time slots through dynamic graphs, boosting classification performance. Furthermore, to address the limitation that the hierarchical representations of graphs are challenging to learn with graph neural networks (GNNs), we introduce a temporal graph pooling layer for obtaining a global graph-level representation. The dynamic graph, graph information propagation, and temporal convolution are jointly learned within an end-to-end framework. Experimental results on 26 UEA benchmark datasets indicate that compared to current state-of-the-art deep learning methods in the MTSC task, the proposed Todynet achieved a performance improvement of 5.5%.

Temporal upscaling of solar radiation components using an analytical model for variability modeling

To properly design systems that harness the solar resource, reliable measurements or estimates of its availability are needed. Often, this data is only available at temporal resolutions that are not sufficiently fine to capture the short-term fluctuations of the solar resource, creating uncertainty in the design and performance assessment of solar-powered systems. Synthetic irradiance generation techniques aim to create high temporal resolution estimates starting from lower resolution data, however, a literature review on the topic shows opportunities for improvement regarding the generality of the generation and evaluation approaches. This work proposes novel methods to estimate a quantifiable indicator of short-term variability from hourly data, a new temporal upscaling methodology for time series of arbitrary origin and destination temporal resolution, based on explicit mathematical functions and sufficient generality for application in different contexts, and an improved assessment methodology. The method was applied tor ground and satellite upscaling up to 1 min, resulting in time series with characteristics consistent with those of a higher temporal resolution and exhibit minimum deviations against the original time series characteristics. This method has applications not only for upscaling of low temporal resolution data, but also for gap-filling techniques and the upscaling of hourly solar radiation forecasts.

Forecasting and uncertainty analysis of tailings dam system safety based on data mining techniques

Tailings dams, as critical infrastructure, play a vital role in ensuring the safety and reliability of tailings pond systems. Predicting the trend of tailings dam monitoring parameters helps monitor its operational status and support safety management and decision-making. The traditional time series prediction model focuses on point prediction while neglecting the nonlinear feature of data and the uncertainty of prediction results, which is far from meeting the requirements for risk monitoring and safety management of tailings dam systems. To address this, Deep Auto-Encoder is used to extract and optimize features of multi-dimensional time series; Whale Optimization Algorithm, Long Short Term Memory, and Kernel Density Estimation are used to build a monitoring parameters trend interval prediction model. It can effectively predict and analyze the uncertainty of tailings dam monitoring parameters. Time series data of monitoring parameters are collected from a tailings dam in Anhui, China and modeled to verify the effectiveness of the model. The results show that the hybrid prediction model performs the best in monitoring parameter prediction accuracy and trend. The Root Mean Square Error values of the Whale Optimization Algorithm and Long Short Term Memory prediction for reservoir water level, surface displacement, internal displacement, phreatic line, and beach width are 0.0160, 1.0405, 0.0389, 0.0176, and 0.7525, with errors reduced by 0.37 % to 17.92 % compared to other models. The uncertainty analysis confirms the high reliability of the model. The Kernel Density Estimation effectively forecasts the fluctuation range of predicted values, significantly improving the prediction effectiveness of the monitoring parameters.

Processing Model of LUCC Temporal Characteristics in Urban and Rural Space Based on Remote Sensing Data Validation

The spatial land change of villages and towns can be analyzed through external spatio-temporal characteristics, which can realize the evaluation of historical development and assist the implementation of planning and design norms and the implementation of problem rectification. To explore the specific process and the overall pattern of land use change, it is necessary to analyze and predict all kinds of data in a unified way to grasp the law of development and the direction of future development. This paper takes the spatial development and planning of villages and towns as the research object, selects the remote sensing image data of regional historical development as the driving factor, combines with Gaussian function fitting to calculate the training samples, and constructs the regional spatial environment analysis system. According to the processing model of external characteristics of time series, the spatial distribution and evolution of Risk-Screening Environmental Indicators (RSEI) and its components are analyzed. The multi-dimensional remote sensing data is innovatively fused into the time series processing model. Besides, the curve which can characterize the spatial change law and is not completely symmetrical is obtained through the least square method fitting so that the data with unequal time series intervals can be flexibly processed. Finally, through the experimental analysis, the traditional Asymmetric Gaussian model (A-G) and Savitzky-Golay (S-G) are taken to compare and analyze the effect of various factors on the processing of time series characteristics. Tests show that the proposed algorithm is superior to the comparison method in noise removal of remote sensing data. The data fitting coefficient is 0.02, and the root mean square measurement error is more than 7% higher than the comparison method. It can predict the trend of land use change distribution, play a key role in assisting decision-making for different land use transformations and utilization in villages and towns, and make a scientific evaluation for the impact of social and economic development potential.