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This paper focuses on water quality prediction in the presence of a large number of missing values in water quality monitoring data. Current water quality monitoring data mostly come from different monitoring stations in different water bodies. As the duration of water quality monitoring increases, the complexity of water quality data also increases, and missing data is a common and difficult to avoid problem in water quality monitoring. In order to fully exploit the valuable features of the monitored data and improve the accuracy of water quality prediction models, we propose a long short-term memory (LSTM) encoder-decoder model that combines a Kalman filter (KF) with an attention mechanism. The Kalman filter in the model can quickly complete the reconstruction and pre-processing of hydrological data. The attention mechanism is added between the decoder and the encoder to solve the problem that traditional recursive neural network models lose long-range information and fully exploit the interaction information among high-dimensional covariate data. Using original data from the Haimen Bay water quality monitoring station in the Lianjiang River Basin for analysis, we trained and tested our model using detection data from 1 January 2019 to 30 June 2020 to predict future water quality. The results show that compared with traditional LSTM models, KF-LSTM models reduce the average absolute error ( M A E ) by 10%, the mean square error ( M S E ) by 21.2%, the root mean square error ( R M S E ) by 13.2%, while increasing the coefficient of determination ( R 2 ) by 4.5%. This model is more suitable for situations where there are many missing values in water quality data, while providing new solutions for real-time management of urban aquatic environments. [ABSTRACT FROM AUTHOR]

At present, time series prediction methods are widely applied for Human–Computer Intelligent Systems in various fields such as Finance, Meteorology, and Medicine. To enhance the accuracy and stability of the prediction model, this paper proposes a time series prediction method called RAdam-Dual stage Attention mechanism-Nested Long Short-Term Memory (RAdam-DA-NLSTM). First, we design a Nested LSTM (NLSTM), which adopts a new internal LSTM unit structure as the memory cell of LSTM to guide memory forgetting and memory selection. Then, we design a self-encoder network based on the Dual stage Attention mechanism (DA-NLSTM), which uses the NLSTM encoder based on the input attention mechanism, and uses the NLSTM decoder based on the time attention mechanism. Additionally, we adopt the RAdam optimizer to solve the objective function, which dynamically selects Adam and SGD optimizers according to the variance dispersion and constructs the rectifier term to fully express the adaptive momentum. Finally, we use multiple datasets, such as PM2.5 data set, stock data set, traffic data set, and biological signals, to analyze and test this method, and the experimental results show that RAdam-DA-NLSTM has higher prediction accuracy and stability compared with other traditional methods. [ABSTRACT FROM AUTHOR]

Sea surface temperature is an important parameter of ocean hydrology. Accurate prediction of sea surface temperature is of great significance for ocean economic development and extreme weather prevention. Application of deep learning-based method in sea surface temperature prediction has significantly increased due to its high analytical power. Nevertheless, sea surface temperature time series are so volatile and stochastic, leading to the fact that in-depth analysis and accurate prediction of sea surface temperature are still challenging. Considering non-stationary and nonlinear characteristics in sea surface temperature sequence, variational mode decomposition is adopted as the de-noising module to reduce the influence of noise. Furthermore, the convolutional neural network is combined with the gated recurrent unit network to extract both spatial and temporal features in sea surface temperature sequence, simultaneously. Finally, a sea surface temperature prediction model based on deep learning model with frequency analysis module is proposed in this paper. The sea surface temperature of East China Sea is selected for empirical study. Comparative analysis demonstrated that the proposed model significantly outperforms the existing sea surface temperature prediction models. Especially, even to the mesoscale level, mean absolute error and root mean squared error of the proposed method are still 0.2038 and 0.2481, respectively. [ABSTRACT FROM AUTHOR]

To effectively improve the power dispatching, the prediction accuracy of wind power has been the concern of many scholars for many years. The wind power prediction problem is actually equivalent to the wind speed prediction problem. Based on linear regression (LR) and variational mode decomposition (VMD), in this paper, we proposed an efficient hybrid method to predict wind speed. In the proposed method, the VMD is used to decompose the signal of wind speed into several sub-signal. Compared with the original wind-speed series, each sub-signal is a more stable subsequence signal. Then, we used the LR method to predict each subsequence signal. Eventually, we obtain the final prediction results of the original wind speed series merged the forecasting values of all subsequences signal. We selected two data to test our proposed method in our experiment. Compared with several comparison methods, we found that our proposed methods has better prediction performance than other methods from the experimental results. [ABSTRACT FROM AUTHOR]

n recent years, there has been significant progress in the fields of Attention-based Transformers[1, 2]. In this paper, we compare the performance of an Attention encoder and an LSTM[3] for the stock prediction task, evaluating the performance based on the model's size and input length. We utilize stock history data for Apple, Samsung, and Amazon, obtained from Kaggle. The Attention Encoder model outperforms the LSTM baseline model on all datasets. This demonstrates the superior performance of the Attention Encoder model. Additionally, increasing the input sequence dimension of the Attention Encoder network leads to improved performance. However, there is a concern regarding overfitting of the Attention Encoder model due to the rapid changes in the data. [ABSTRACT FROM AUTHOR]

The prediction of wind in terms of both wind speed and direction, which has a crucial impact on many real-world applications like aviation and wind power generation, is extremely challenging due to the high stochasticity and complicated correlation in the weather data. Existing methods typically focus on a sub-set of influential factors and thus lack a systematic treatment of the problem. In addition, fine-grained forecasting is essential for efficient industry operations, but has been less attended in the literature. In this work, we propose a novel data-driven model, multi-horizon spatiotemporal network (MHSTN), generally for accurate and efficient fine-grained wind prediction. MHSTN integrates multiple deep neural networks targeting different factors in a sequence-to-sequence (Seq2Seq) backbone to effectively extract features from various data sources and produce multi-horizon predictions for all sites within a given region. MHSTN is composed of four major modules. First, a temporal module fuses coarse-grained forecasts derived by numerical weather prediction (NWP) and historical on-site observation data at stations so as to leverage both global and local atmospheric information. Second, a spatial module exploits spatial correlation by modeling the joint representation of all stations. Third, an ensemble module weighs the above two modules for final predictions. Furthermore, a covariate selection module automatically choose influential meteorological variables as initial input. MHSTN is already integrated into the scheduling platform of one of the busiest international airports of China. The evaluation results demonstrate that our model outperforms competitors by a significant margin. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the capability of modeling distant temporal interaction of Long Short-Term Memory (LSTM) and introduce a novel Long Short-Term Memory on time series problems. To increase the capability of modeling distant temporal interactions, we propose a hierarchical architecture (HLSTM) using several LSTM models and a linear layer. This novel framework is then applied to electric power consumption, real-life crime and financial data. We demonstrate in our simulations that this structure significantly improves the modeling of deep temporal connections compared to the classical architecture of LSTM and various studies in the literature. Furthermore, we analyze the sensitivity of the new architecture with respect to the hidden size of LSTM. [ABSTRACT FROM AUTHOR]

Accurately predicting traffic flow on roads is crucial to address urban traffic congestion and save on travel time. However, this is a challenging task due to the strong spatial and temporal correlations of traffic data. Existing traffic flow prediction methods based on graph neural networks and recurrent neural networks often overlook the dynamic spatiotemporal dependencies between road nodes and excessively focus on the local spatiotemporal dependencies of traffic flow, thereby failing to effectively model global spatiotemporal dependencies. To overcome these challenges, this article proposes a new Spatio-temporal Causal Graph Attention Network (STCGAT). STCGAT utilizes a node embedding technique that enables the generation of spatial adjacency subgraphs on a per-time-step basis, without requiring any prior geographic information. This obviates the necessity for intricate modeling of constantly changing graph topologies. Additionally, STCGAT introduces a proficient causal temporal correlation module that encompasses node-adaptive learning, graph convolution, as well as local and global causal temporal convolution modules. This module effectively captures both local and global Spatio-temporal dependencies. The proposed STCGAT model is extensively evaluated on traffic datasets. The results show that it outperforms all baseline models consistently. [ABSTRACT FROM AUTHOR]

Time series forecasting using multilayer feed-forward neural networks (FNN) is potential to give high accuracy. Several factors influence the accuracy. One of them is the choice of activation functions (AFs). There are several AFs commonly used in FNN with their specific characteristics, such as bounded type AFs. They include sigmoid, softsign, arctan, and tanh. This paper investigates the effect of the amplitude in the bounded AFs on the FNNs' accuracy. The theoretical investigations use simplified FNN models: linear equation and linear combination. The results show that the higher amplitudes give higher accuracy than typical amplitudes in softsign, arctan, and tanh AFs. However, in sigmoid AF, the amplitude changes do not influence the accuracy. These theoretical results are supported by experiments using the FNN model for time series prediction of 10 foreign exchanges from different continents compared to the US dollar. Based on the experiments, the optimum amplitude of the AFs should be high, that is greater or equal to 100 times of the maximum input values to the FNN, and the accuracy gains up to 3--10 times. [ABSTRACT FROM AUTHOR]

With the rapid development of meteorology, there requires a great need to better forecast dew point temperatures contributing to mild building surface and rational chemical control, while researches on time series forecasting barely catch the attention of meteorology. This paper would employ the seasonal-trend decomposition-based simplified dendritic neuron model (STLDNM*) to predict the dew point temperature. We utilize the seasonal-trend decomposition based on LOESS (STL) to extract three subseries from the original sequence, among which the residual part is considered as an input of an improved dendritic neuron model (DNM*). Then the back-propagation algorithm (BP) is used for training DNM* and the output is added to another two series disposed. Four datasets, which record dew points of four cities, along with eight algorithms are put into the experiments for comparison. Consequently, the combination of STL and simplified DNM achieves the best speed and accuracy.

Abstract In this work, a high efficient next generation reservoir computing (HENG‐RC) paradigm that adopts the principle of local states correlation and attention mechanism is proposed, which is able to process dynamical information generated by both the low dimensional and very large spatiotemporal chaotic systems (VLSCS). From a dynamical system perspective, the dynamical characteristics such as density distribution, Poincaré plots and max Lyapunov exponents of the proposed HENG‐RC are studied. It is revealed that the trained model can be seen as a data‐driven chaotic system. Furthermore, a novel scheme of secure communication based on chaotic synchronization of two HENG‐RC systems is designed, of which the security is enhanced as the intruder needs to know simultaneously the training signal and details of the parameter setting in the HENG‐RC. The digital implementation using field programmable gate array is experimentally realised, proving the feasibility of the secure communication scheme.

Nowadays, China is developing new energy industries to reduce carbon emissions to meet the challenge of world climate change, so investors can consider to invest in stocks of Chinese new energy companies to gain income. In order to study how to forecast stock closing prices of new energy companies in China, we have chosen 12 representative companies, and first used autoregressive univariate time series models to predict the trends of the stock closing prices in the next month. The results show that Seasonal Autoregressive Integrated Moving Average model has the best out-of-sample trend prediction effect. Second, we use multivariate time series forecasting models to predict the stock closing prices of each day through external variables. The results show that Temporal Convolutional Attention Neural Networks has the best effect of out-of-sample prediction. We recommend that investors who are interested in investing in new energy companies in China first use the Seasonal Autoregressive Integrated Moving Average model to predict the short-term stock closing price trend in the future, and then use the Temporal Convolutional Attention Neural Networks model to predict the stock closing price on the next day to decide whether to invest.

Heartbeat serves as a vital sign of health, aiding the diagnosis of various health issues. The autonomic nervous system (ANS) is responsible for regulating heartbeat, and physical activity (PA) can influence the heart rhythm. Changes in heart rate can signal alterations in PA. However, poor measurements or extreme conditions can result in the loss of heartbeat data, which can negatively impact the analysis of heartbeats. To accurately monitor the Heart Rate Variability (HRV) and cardiovascular health, a proper model to compensate for lost data is necessary. This study investigated the effect of different PAs on InterBeat Interval (IBI) prediction and the possibility of using models trained on unrelated activities to predict the next IBI. The IBI series is divided into piecewise stationary sections based on PA, that is, running, walking, and sitting, as verified by a statistical test. Various machine and deep learning methods have been used to model the IBIs related to specific activities. The models were then used to predict the next IBI for the testing sets of related and unrelated activities, and the error changes were compared for each permutation of training and testing. The models were tested using a Physionet archived dataset. The findings suggest that linear models offer the least prediction error, whereas PA-relevant training could minimize errors in most scenarios. However, in cases where specific PA data are not available, the proposed CNN model demonstrated superior generalization capabilities. These findings can improve HRV error correction techniques and enhance cardiovascular health and ANS monitoring.

Malicious software or misbehaving applications have the potential to trigger signaling storms on mobile networks, leading to battery drainage on devices and causing bandwidth overuse at the cell level. Additionally, these storms may result in an excessive signaling load within the mobile operator’s infrastructure. This paper uses a combination of time series prediction, adaptive threshold, and anomaly detection algorithms to predict signaling storms. Whether a signaling storm will be triggered in the future can be determined based on the fluctuation pattern of the data. Our method enables us to identify the top cause of the signaling storm in advance, so that the network optimization team can address the issues that will arise in advance, maximizing the stop-loss. The time series prediction algorithm has significant advantages over the moving average and TFT(Temporal Fusion Transformers), with a WAPE(Weighted Absolute Percentage Error) of only 0.09. Adaptive threshold can avoid treating holiday data as abnormal data, and the accuracy of anomaly detection based on the automatic adaptive threshold is higher than the traditional fixed threshold. In addition, combining the signaling conduction chain can also perform top cause localization to identify the upstream network element instance that first encountered the problem. The entire algorithm not only performs well in the current network but also performs well in artificially generated signaling storm data, pioneering the field of signaling storm prediction.

The soybean futures market in China occupies an important position in the agricultural product futures market. The research on the fluctuation of soybean futures price and the prediction of the future price trend has always been the focus of extensive attention in the field of agricultural economics. This paper proposes an EEMD-NAGU hybrid prediction model for soybean futures price based on Ensemble Empirical Mode Decomposition (EEMD) and New Attention Gate Unit (NAGU). The model first uses EEMD to process soybean futures price data into multiple Intrinsic Mode Functions (IMFs) and residual sequence. Then calculates the sample entropy of each IMF and reconstructed the IMF into three components of low-frequency, medium-frequency, and high-frequency, according to the size of the sample entropy. NAGU is formed by embedding the Attention mechanism (Attention) into the Gate Recurrent Unit (GRU) structure, further improving the learning capability of the model. NAGU splits the original reset gate and update gate, sets up two-stage respectively, and uses different activation functions to capture the information in historical data better. Soybean futures price data is complex nonlinearities and contain more noise. In this model, EEMD plays denoises the time series data and fixes the model input. NAGU can perform differential learning on data and finally produce prediction results. EEMD-NAGU is compared with thirteen other prediction models (Support Vector Regression (SVR), LSTM, GRU, NAGU, EEMD-LSTM, EEMD-GRU, EEMD-NGU, Attention-LSTM, Attention-GRU, Attention-NGU, EEMD-Attention-LSTM, EEMD-Attention-GRU, and EEMD-Attention-NGU). The evaluation indexes of the experiment are Mean Absolute Error (MAE), Mean Square Error (MSE), and R Squared ( $R^{2}$ ). The experimental results show that the EEMD-NAGU model outperforms other models with better prediction performance. The model can be widely used to predict the price of wheat, corn, gold, oil, and other time series data.

Pigeon is a kind of poultry with high practicability and economic value, and humidity is an important indicator of the intensive breeding of pigeons, which is closely related to the healthy growth of pigeons. Humidity changes are complex, dynamic, and nonlinear. Accurately predicting humidity values and analyzing their changing trends is crucial to the growth of pigeons. Addressing low prediction accuracy and poor generalization of traditional humidity prediction methods, this paper proposes a combination based on partial least squares (PLS) data dimensionality reduction, Savitzky-Golay (SG) filter, and sparrow optimization algorithm (SSA) Predictive models to improve humidity forecast accuracy. First, the SG filter is used for data smoothing to remove abnormal noise in the data signal to enhance the feature learning ability of the prediction system. Next, the original data is selected through the PLS method to select information higher than the set threshold. Valuable information features are extracted for data dimensionality reduction. In addition, SSA is used to optimize key parameters and structure of the BiGRU model for improved prediction. Finally, a combined prediction model based on SG-PLS-SSA-BiGRU was built to simulate and predict the humidity of the pigeon house. The experimental results demonstrate the proposed model’s high accuracy and stability with minimal prediction error fluctuations, fast calculation speed, good feature extraction ability and generalization ability, and is very effective and reliable for predicting pigeon house breeding humidity.

Coal mine roof accidents pose a significant threat to the safety of personnel and equipment. To mitigate these risks, this study presents an improved time-series prediction model, called Nadam-LSTM, for mining pressure data at the working face in coal mines. The Nadam-LSTM model combines long and short-term memory neural networks (LSTM) with the Nadam optimization algorithm to accurately forecast mining pressure and enhance safety measures. The model incorporates multiple parameters by utilizing factor analysis to identify the key variables influenced by pressure and correlation analysis to quantify their relationships. The Nadam-LSTM model takes into account various input factors, including mining height, coal thickness, burial depth, coal seam dip angle, working face length, roof lithology, and mining speed. The model provides predictions for important output variables, such as first pressure distance, first pressure intensity, periodic pressure distance, and periodic pressure intensity. To further enhance performance, the Nadam optimization algorithm and Dropout regularization are integrated into the model, enabling the construction of a control group prediction model and a mining pressure prediction optimization tool.The performance of the Nadam-LSTM model is evaluated using root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R), and R-squared (R^2). Furthermore, comparative analyses are conducted using Boxplot, Scatterplot, Violin plot, and Taylor diagram to assess and showcase the model’s performance. These evaluations provide comprehensive insights into the model’s accuracy, robustness, and predictive capabilities. Evaluation results demonstrate that the Nadam-LSTM model outperforms a single LSTM model, with significant changes observed in multiple indicators. Such as MAE and RMSE. Also, the inclusion of R and R^2 values provide a measure of the model’s goodness-of-fit. Comparative visualizations, including Boxplot, Scatterplot, Violin plot, and Taylor diagram, further highlight the improved performance and effectiveness of the Nadam-LSTM model in accurately forecasting mining pressure.

Time series prediction is an effective tool for marine scientific research. The Hierarchical Temporal Memory (HTM) model has advantages over traditional recurrent neural network (RNN)-based models due to its online learning and prediction capabilities. Given that the neuronal structure of HTM is ill-equipped for the complexity of long-term marine time series applications, this study proposes a new, improved HTM model, incorporating Gated Recurrent Units (GRUs) neurons into the temporal memory algorithm to overcome this limitation. The capacities and advantages of the proposed model were tested and evaluated on time series data collected from the Xiaoqushan Seafloor Observatory in the East China Sea. The improved HTM model both outperforms the original one in short-term and long-term predictions and presents results with lower errors and better model stability than the GRU model, which is proficient in long-term predictions. The findings allow for the conclusion that the mechanism of online learning has certain advantages in predicting ocean observation data.

Quantum Recurrent Neural Networks are receiving an increased attention thanks to their enhanced generalization capabilities in time series analysis. However, their performances were bottlenecked by long training times and unscalable architectures. In this paper, we propose a novel Quantum Recurrent Neural Network model based on Quantum Gated Recurrent Units. It uses a learnable Variational Quantum Layer to process temporal data, interspersed with two classical layers to properly match the dimensionality of the input and output vectors. Such an architecture has fewer quantum parameters than existing Quantum Long Short-Term Memory models. Both the quantum networks were evaluated on periodic and real-world time series datasets, together with the classical counterparts. The quantum models exhibited superior performances compared to the classical ones in all the test cases. The Quantum Gated Recurrent Units outperformed the Quantum Long Short-Term Memory network despite having a simpler internal configuration. Moreover, the Quantum Gated Recurrent Units network demonstrated to be about 25% faster during the training and inference procedure over the Quantum Long Short-Term Memory. This improvement in speed comes with one less quantum circuit to be executed, suggesting that our model may offer a more efficient alternative for implementing Quantum Recurrent Neural Networks on both simulated and real quantum hardware.

The climatic parameters within greenhouse facilities, such as temperature, humidity, and light, exert significant influence on the growth and yield of crops, particularly seedlings. Therefore, it is crucial to establish an accurate predictive model to monitor and adjust the greenhouse microclimate for optimizing the greenhouse environment to the fullest extent. To precisely forecast the greenhouse microclimate and assess the suitability of nursery environments, this study focuses on greenhouse environmental factors. This study leveraged open-source APIs to acquire meteorological data, integrated a model based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM), and utilized the sparrow search algorithm to optimize model parameters, consequently developing a time series greenhouse microclimate prediction model. Furthermore, Squeeze-and-Excitation (SE) Networks were employed to enhance the model’s attention mechanism, enabling more accurate predictions of environmental factors within the greenhouse. The predictive results indicated that the optimized model achieved high precision in forecasting the greenhouse microclimate, with average errors of 0.540 °C, 0.936%, and 1.586 W/m2 for temperature, humidity, and solar radiation, respectively. The coefficients of determination (R2) reached 0.940, 0.951, and 0.936 for temperature, humidity, and solar radiation, respectively. In comparison to individual CNN or LSTM models, as well as the back-propagation (BP) neural network, the proposed model demonstrates a significant improvement in predictive accuracy. Moreover, this research was applied to the greenhouse nursery environment, demonstrating that the proposed model significantly enhanced the efficiency of greenhouse seedling cultivation and the quality of seedlings. Our study provided an effective approach for optimizing greenhouse environmental control and nursery environment suitability, contributing significantly to achieving sustainable and efficient agricultural production.

Adaptive cruise control and autonomous lane-change systems represent pivotal advancements in intelligent vehicle technology. To enhance the operational efficiency of intelligent vehicles in combined lane-change and car-following scenarios, we propose a coordinated decision control model based on hierarchical time series prediction and deep reinforcement learning under the influence of multiple surrounding vehicles. Firstly, we analyze the lane-change behavior and establish boundary conditions for safe lane-change, and divide the lane-change trajectory planning problem into longitudinal velocity planning and lateral trajectory planning. LSTM network is introduced to predict the driving states of surrounding vehicles in multi-step time series, combining D3QN algorithm to make decisions on lane-change behavior. Secondly, based on the following state between the ego vehicle and the leader vehicle in the initial lane, as well as the relationship between the initial distance and the expected distance with the leader vehicle in the target lane, with the primary objective of maximizing driving efficiency, longitudinal velocity is planned based on driving conditions recognition. The lateral trajectory and conditions recognition are then planned using the GA-LSTM-BP algorithm. In contrast to conventional adaptive cruise control systems, the DDPG algorithm serves as the lower-level control model for car-following, enabling continuous velocity control. The proposed model is subsequently simulated and validated using the NGSIM dataset and a lane-change scenarios dataset. The results demonstrate that the algorithm facilitates intelligent vehicle lane-change and car-following coordinated control while ensuring safety and stability during lane-changes. Comparative analysis with other decision control models reveals a notable 17.58% increase in driving velocity, underscoring the algorithm’s effectiveness in improving driving efficiency.

Economic forecasting is crucial since it benefits many different parties, such as governments, businesses, investors, and the general public. This paper presents a novel methodology for forecasting business cycles using an autoregressive integrated moving average (ARIMA), a popular linear model in time series forecasting, and a neural network with weighted fuzzy membership functions (NEWFM) as a forecasting model generator. The study used a dataset that included seven components of the leading composite index, which is used to predict positive or negative trends in several economic sectors before the GDP is compiled. The preprocessed time series data comprising the leading composite index using ARIMA were used as input vectors for the NEWFM to predict comprehensive business fluctuations. The prediction capability significantly improved through the duplicated refining process of the dataset using ARIMA and NEWFM. The combined ARIMA and NEWFM techniques exceeded ARIMA in both classification and prediction, yielding an accuracy of 91.61%.

This paper addresses the challenges in forecasting electrical energy in the current era of renewable energy integration. It reviews advanced adaptive forecasting methodologies while also analyzing the evolution of research in this field through bibliometric analysis. The review highlights the key contributions and limitations of current models with an emphasis on the challenges of traditional methods. The analysis reveals that Long Short-Term Memory (LSTM) networks, optimization techniques, and deep learning have the potential to model the dynamic nature of energy consumption, but they also have higher computational demands and data requirements. This review aims to offer a balanced view of current advancements and challenges in forecasting methods, guiding researchers, policymakers, and industry experts. It advocates for collaborative innovation in adaptive methodologies to enhance forecasting accuracy and support the development of resilient, sustainable energy systems.

Introduction: This paper proposes a deep learning algorithm based on the VMD-SSA-BiLSTM model for time series forecasting in the smart grid financial market. The algorithm aims to extract useful information from power grid signals to improve the timing prediction accuracy and meet the needs of sustainable innovation management.Methods: The proposed algorithm employs the variational mode decomposition (VMD) method to decompose and reduce the dimensionality of historical data, followed by singular spectrum analysis (SSA) to perform singular spectrum analysis on each intrinsic mode function component. The resulting singular value spectrum matrices serve as input to a bidirectional long short-term memory (BiLSTM) neural network, which learns the feature representation and prediction model of the smart grid financial market through forward propagation and backpropagation.Results: The experimental results demonstrate that the proposed algorithm effectively predicts the smart grid financial market's time series, achieving high prediction accuracy and stability. The approach can contribute to sustainable innovation management and the development of the smart grid.Discussion: The VMD-SSA-BiLSTM algorithm's efficiency in extracting useful information from power grid signals and avoiding overfitting can improve the accuracy of timing predictions in the smart grid financial market. The algorithm's broad application prospects can promote sustainable innovation management and contribute to the development of the smart grid.

Data concerning product sales are a popular topic in time series forecasting due to their multidimensionality and wide presence in many businesses. This paper describes the research in predicting the timing and product category of the next purchase based on historical customer transaction data. Given that the dataset was acquired from a vendor of medical drugs and devices, the generic product identifier (GPI) classification system was incorporated in assigning product categories. The models built are based on recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks with different input and output features, and training datasets. Experiments with various datasets were conducted and optimal network structures and types for predicting both product category and next purchase day were identified. The key contribution of this research is the process of data transformation from its original purchase transaction format into a time series of input features for next purchase prediction. With this approach, it is possible to implement a dedicated personalized marketing system for a vendor. [ABSTRACT FROM AUTHOR]

Collaborative robots have gained popularity in industries, providing flexibility and increased productivity for complex tasks. However, their ability to interact with humans and adapt to their behavior is still limited. Prediction of humanmovement intentions is one way to improve the robots adaptation. This paper investigates the performance of using Transformers and MLP-Mixer based neural networks to predict the intended human arm movement direction, based on gaze data obtained in a virtual reality environment, and compares the results to using an LSTM network. The comparison will evaluate the networks based on accuracy on several metrics, time ahead of movement completion, and execution time. It is shown in the paper that there exists several network configurations and architectures that achieve comparable accuracy scores. The best performing Transformers encoder presented in this paper achieved an accuracy of 82.74%, for predictions with high certainty, on continuous data and correctly classifies 80.06% of the movements at least once. The movements are, in 99% of the cases, correctly predicted the first time, before the hand reaches the target and more than 19% ahead of movement completion in 75% of the cases. The results shows that there are multiple ways to utilize neural networks to perform gaze based arm movement intention prediction and it is a promising step toward enabling efficient human-robot collaboration. [ABSTRACT FROM AUTHOR]

• A new grey intelligent prediction algorithm with the multiobjective correction strategy is proposed. • A new weakened accumulation operation is proposed. • A new evaluation system for evaluating the performance of prediction models is constructed. As a combination of the differential equation prediction model and intelligent optimization algorithm, the grey intelligent prediction algorithm has attracted increasing attention due to its outstanding performance in small-sample environments. However, most studies focus only on the improvement of algorithm performance, with little regard to the uniformity, ill-condition and overfitting of the algorithm. To promote the development of this field, we develop a new grey intelligent prediction algorithm with multiobjective correction strategy based on the new weakened accumulation grey optimization model and the multiobjective grey wolf optimizer. In this new prediction algorithm, the new weakened accumulation operation is utilized to enhance the predictive ability of the algorithm and mitigate the ill-condition of the system, the Bernoulli parameter and a discretization technique are used to activate the uniformity and unbiasedness of the algorithm, respectively, and the multiobjective grey wolf optimizer is employed to alleviate the overfitting of the system. Compared with the previous grey intelligent prediction models, the new prediction algorithm is more perfect and reasonable. Taking two energy datasets as research cases, the evaluation results of a system consisting of six evaluation metrics and the Diebold-Mariano test show that the proposed model outperforms the other six comparative models in terms of prediction performance and stability, which confirms the feasibility and validity of the algorithm. [ABSTRACT FROM AUTHOR]

One of the main indicators for detecting changes in climate and marine ecosystems around the world is the sea surface temperature (SST). Even with several models presented in the literature, it is still a challenging task when only a single model is considered for SST forecasting. In this context, hybrid approaches that combine statistical models (to estimate linear dependencies) and machine learning models (to estimate nonlinear dependencies from residuals) have attained highlighted accuracy in several time series forecasting problems. In this way, this paper proposes a hybrid system that combines the autoregressive integrated moving average (ARIMA) model with a deep morphological neural network model, which employs dilation-erosion operators to estimate ARIMA's model residuals. In this context, the proposed hybrid system is composed of three stages: (1) time series forecast using the ARIMA model, (2) residuals forecast using the deep morphological neural network and (3) a linear combination of the stages (1) and (2). Three SST time series are used in the experimental analysis. Moreover, the achieved results are evaluated using three relevant statistical measures showing that the proposed hybrid system attains higher accuracy when compared to other hybrid models in the literature. [ABSTRACT FROM AUTHOR]

Forecasting container throughput is critical for improved port planning, operations, and investment strategies. Reliability of forecasting methods need to be ensured before utilizing their outcomes in decision making. This study compares forecasting performances of various time series methods, namely autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), Holt-Winter's Exponential Smoothing (HWES), and the Prophet model. Since forecast combinations can improve performance, simple and weighted combinations of ARIMA, SARIMA and HWES have been explored, too. Monthly container throughput data of port of Shanghai, Busan, and Nagoya are used. The Prophet model outperforms others in the in-sample forecasting, while combined models outperform others in the out-sample forecasting. Due to the observed differences between the in-sample and out-sample forecast accuracy measures, this study proposes a forecast performance metric consistency check approach for informed real-world applications of forecasting models in port management decision-making. [ABSTRACT FROM AUTHOR]

Nowadays, air pollution is a big environmental problem in developing countries. In this problem, particulate matter 2.5 (PM2.5) in the air is an air pollutant.When its concentration in the air is high in developing countries like Vietnam, it will harm everyone's health. Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen. This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City, Vietnam. Firstly, this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC dataset. Only variables that affect the results will be selected for PM2.5 concentration prediction. Secondly, an efficient PM25-CBL model that integrates a convolutional neural network (CNN) and Bidirectional Long Short-TermMemory (Bi-LSTM) is developed. This model consists of three following modules: CNN, Bi-LSTM, and Fully connected modules. Finally, this study conducts the experiment to compare the performance of our approach and several state-of-the-art deep learning models for time series prediction such as LSTM, Bi-LSTM, the combination of CNN and LSTM (CNN-LSTM), and ARIMA. The empirical results confirm that PM25-CBL model outperforms other methods for Air Quality HCMC dataset in terms of several metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). [ABSTRACT FROM AUTHOR]

Tensor time series shows the co-evolution characteristics of multiple time series with time changes, and tensor time series prediction has become an important problem. Aiming at the high-dimensional characteristics of tensor time series, T-Transformer, a tensor time series prediction method based on Transformer, is proposed, which integrates tensor operation and Transformer into a unified framework. The method first uses tensors to represent higher-order time series, uses tensor slices and vectorization to convert tensor time series into vectors, and then inputs the vectors into Transformer model after coding, and finally obtains the predicted value of tensor time series. Experiments on three open data sets show that the proposed method achieves better prediction results than the benchmark method. [ABSTRACT FROM AUTHOR]

Artificial Neural Network (ANN) is an effective machine learning technique for addressing regression tasks. Nonetheless, the performance of ANN is highly dependent on the values of its parameters, specifically the weight and bias. To improve its predictive generalization, it is crucial to optimize these parameters. In this study, the Barnacles Mating Optimizer (BMO) is employed as an optimization tool to automatically optimize these parameters. As a relatively new optimization algorithm, it has been shown to be effective in addressing various optimization problems. The proposed hybrid predictive model of BMO-ANN is tested on time series data of stock price using six selected inputs to predict the next day’ closing prices. Evaluated based on Mean Square Error (MSE) and Root Mean Square Error (RMSPE), the proposed BMO-ANN exhibits significant superiority over the other identified hybrid algorithms. Additionally, the difference in means between BMO-ANN and other identified hybrid algorithms was found to be statistically significant, with a significance level of 0.05%. [ABSTRACT FROM AUTHOR]

In this paper, we present a feasibility study on predicting the torque signal of a passenger car with the help of a neural network. In addition, we analyze the possibility of using the proposed model structure for temperature prediction. This was carried out with a neural network, specifically a three-layer long short-term memory (LSTM) network. The data used were real road load data from a Jaguar Land Rover Evoque with a Twinster gearbox from GKN. The torque prediction generated good results with an accuracy of 55% and a root mean squared error ( R M S E ) of 49 Nm, considering that the data were not generated under laboratory conditions. However, the performance of predicting the temperature signal was not satisfying with a coefficient of determination ( R 2 ) score of −1.396 and an R M S E score of 69. 4 °C. The prediction of the torque signal with the three-layer LSTM network was successful but the transferability of the network to another signal (temperature) was not proven. The knowledge gained from this investigation can be of importance for the development of virtual sensor technology. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The forecasting of building energy consumption remains a challenging task because of the intricate management of the relevant parameters that can influence the performance of models. Due to the powerful capability of artificial intelligence (AI) in forecasting problems, it is deemed to be highly effective in this domain. However, achieving accurate predictions requires the extraction of meaningful historical knowledge from various features. Given that the exogenous data may affect the energy consumption forecasting model's accuracy, we propose an approach to study the importance of data and selecting optimum time lags to obtain a high-performance machine learning-based model, while reducing its complexity. Regarding energy consumption forecasting, multilayer perceptron-based nonlinear autoregressive with exogenous inputs (NARX), long short-term memory (LSTM), gated recurrent unit (GRU), decision tree, and XGboost models are utilized. The best model performance is achieved by LSTM and GRU with a root mean square error of 0.23. An analysis by the Diebold–Mariano method is also presented, to compare the prediction accuracy of the models. In order to measure the association of feature data on modeling, the "model reliance" method is implemented. The proposed approach shows promising results to obtain a well-performing model. The obtained results are qualitatively reported and discussed. [ABSTRACT FROM AUTHOR]

Wind speed prediction is very important in the field of wind power generation technology. It is helpful for increasing the quantity and quality of generated wind power from wind farms. By using univariate wind speed time series, this paper proposes a hybrid wind speed prediction model based on Autoregressive Moving Average-Support Vector Regression (ARMA-SVR) and error compensation. First, to explore the balance between the computation cost and the sufficiency of the input features, the characteristics of ARMA are employed to determine the number of historical wind speeds for the prediction model. According to the selected number of input features, the original data are divided into multiple groups that can be used to train the SVR-based wind speed prediction model. Furthermore, in order to compensate for the time lag introduced by the frequent and sharp fluctuations in natural wind speed, a novel Extreme Learning Machine (ELM)-based error correction technique is developed to decrease the deviations between the predicted wind speed and its real values. By this means, more accurate wind speed prediction results can be obtained. Finally, verification studies are conducted by using real data collected from actual wind farms. Comparison results demonstrate that the proposed method can achieve better prediction results than traditional approaches. [ABSTRACT FROM AUTHOR]

Many prediction methods proposed in the literature can be concerned under two main headings: probabilistic and non-probabilistic methods. In particular, as a kind of non-probabilistic model, artificial neural networks (ANNs), having different properties, have been commonly and effectively used in the literature. Some ANNs operate the additive aggregation function in the structure of their neuron models, while others employ the multiplicative aggregation function. Recently proposed dendritic neural networks also have both additional and multiplicative neuron models. The prediction performance of such an artificial neural network will inevitably be negatively affected by the outliers that the time series of interest may contain due to the neuron model in its structure. This study, for the training of a dendritic neural network, presents a robust learning algorithm. The presented robust algorithm is the first for the training of DNM in the literature as far as is known and uses Huber's loss function as the fitness function. The iterative process of the robust learning algorithm is carried out by particle swarm optimization. The productivity and efficiency of the suggested learning algorithm were evaluated by analysing different real-life time series. All analyses were performed with original and contaminated data sets under different scenarios. The R-DNM has the best performance for the original data sets with a value of 2.95% in the ABC time series, while the FTSE showed the best performance in approximately 27% and the second best in 33% of all analyses. The proposed R-DNM has been the least affected by outliers in almost all scenarios for contaminated ABC data sets. Moreover, it has been the least affected model by outliers in approximately 71% of the 90 analyses performed for the contaminated FTSE time series. The obtained results show that the dendritic artificial neural network trained by the proposed robust learning algorithm produces the satisfactory predictive results in the analysis of time series with and without outliers. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Forecasting flight fares is a critical task in the rapidly expanding civil aviation industry and involves numerous factors. However, traditional airfare prediction systems are ineffective due to the complex and nonlinear relationships of multiple factors, which are not able to accurately account for the impact of different attributes such as time period. To tackle these issues, in this study, we proposed a novel approach that utilizes a deep-learning model, specifically, the Gated Recurrent Unit (GRU), by incorporating 44 decision features. The proposed model is able to capture the intricate relationships between various factors effectively and predict air ticket prices with high accuracy. In the experiments, it was found that the GRU model significantly outperforms not only classic machine learning models but also the MLP and LSTM in terms of assessment indicators of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The GRU model is thus promising concerning the fare prediction of flight tickets. [ABSTRACT FROM AUTHOR]

The analysis of time series is essential in many fields, and reservoir computing (RC) can provide effective temporal processing that makes it well-suited for time series analysis and prediction tasks. In this study, we introduce a new discrete memristor model and a corresponding two-dimensional hyperchaotic map with complex dynamic properties that are well-suited for reservoir computing. By applying this map to the RC, we enhance the state richness of the reservoir, resulting in improved performance. The paper evaluates the performance of the proposed RC approach using time series data for sunspot, exchange rate, and solar-E forecasting tasks. Our experimental results demonstrate that this approach is highly effective in handling temporal data with both accuracy and efficiency. And comparing with other discrete memristive chaotic maps, the proposed map is the best for improving the RC performance. Furthermore, the proposed RC model is characterized by a simple structure that enables it to fully exploit the time-dependence of the state values of the hyperchaotic map. [ABSTRACT FROM AUTHOR]

Earth orientation parameters (EOPs) are essential in geodesy, linking the terrestrial and celestial reference frames. Due to the time needed for data processing and combining different space geodetic techniques, EOPs of the highest quality suffer latencies from several days to several weeks. However, real-time EOPs are needed for multiple geodetic and geophysical applications. Predictions of EOPs in the ultra-short term can overcome the latency of EOP products to a certain extent. Traditionally, predictions are performed using statistical methods. With the rapid expansion of computing capacity and data volume, the application of deep learning in geodesy has become increasingly promising in recent years. In particular, the Long Short-Term Memory (LSTM) neural networks, one of the most popular Recurrent Neural Network varieties, are promising for geodetic time series prediction. In this study, we investigate the potential of using LSTM to predict daily length of day (LOD) variations up to ten days in advance, accounting for the contribution of effective angular momentum (EAM). The data are first preprocessed to obtain residuals by combining physical and statistical models. Then, we employ LSTM networks to predict the LOD residuals using both LOD and EAM residuals as input features. Our methods outperform all other state-of-the-art methods in the first eight days with an improvement of up to 43% under the first EOP Prediction Comparison Campaign conditions. In addition, we assess the performance of LOD predictions using more extended time series to consider the improvements of EOP products over the last decade. The results show that extending data volume significantly increases the performance of the methods. [ABSTRACT FROM AUTHOR]

With the rapid development of meteorology, there requires a great need to better forecast dew point temperatures contributing to mild building surface and rational chemical control, while researches on time series forecasting barely catch the attention of meteorology. This paper would employ the seasonal-trend decomposition-based simplified dendritic neuron model (STLDNM*) to predict the dew point temperature. We utilize the seasonal-trend decomposition based on LOESS (STL) to extract three subseries from the original sequence, among which the residual part is considered as an input of an improved dendritic neuron model (DNM*). Then the back-propagation algorithm (BP) is used for training DNM* and the output is added to another two series disposed. Four datasets, which record dew points of four cities, along with eight algorithms are put into the experiments for comparison. Consequently, the combination of STL and simplified DNM achieves the best speed and accuracy. [ABSTRACT FROM AUTHOR]

During the deployment of practical applications, reservoir computing (RC) is highly susceptible to radiation effects, temperature changes, and other factors. Normal reservoirs are difficult to vouch for. To solve this problem, this paper proposed a random adaptive fault tolerance mechanism for an echo state network, i.e., RAFT-ESN, to handle the crash or Byzantine faults of reservoir neurons. In our consideration, the faulty neurons were automatically detected and located based on the abnormalities of reservoir state output. The synapses connected to them were adaptively disconnected and withdrawn from the current computational task. On the widely used time series with different sources and features, the experimental results show that our proposal can achieve an effective performance recovery in the case of reservoir neuron faults, including prediction accuracy and short-term memory capacity (MC). Additionally, its utility was validated by statistical distributions. [ABSTRACT FROM AUTHOR]

High requirements are put forward for the settlement control of metro tunnel to ensure the normal and safe operation of adjacent metro line during the process of deep foundation pit construction. Monitoring and predicting could constantly monitor the settlement of the tunnel and make safety early-warning, and massive data to be processed is collected by sensors in this process. In the study, an improved clustering method based on Gaussian mixture model (GMM) is proposed to deal with a large amount of monitoring data. Four initial eigenvalues are defined and the initial core points of clustering are selected by grouping monitoring sensors based on the characteristics of the project site and sensors. An improved method is utilized to the metro tunnel of Metro Line 9 near Xujiahui station. Compared with the traditional clustering method, the improved method has more reliable results, and reduces the operation time by 57.9%. Representative monitoring sensors are selected from each cluster to predict based on Long Short-Term Memory (LSTM) neural network. The prediction results well agree with the measured value and the prediction accuracy is reaching to 99.3%. Compared with other sensor selection ways, the data of representative sensors exhibits good representativeness and effectiveness. Finally, the prediction result after data update is more consistent with the monitoring data than the prediction result without data update. Increasing the data update frequency improves the accuracy of the prediction results in practical engineering application. [ABSTRACT FROM AUTHOR]

Appropriate cooling of the converter valve in a high-voltage direct current (HVDC) transmission system is highly significant for the safety, stability, and economical operation of a power grid. The proper adjustment of cooling measures is based on the accurate perception of the valve's future overtemperature state, which is characterized by the valve's cooling water temperature. However, very few previous studies have focused on this need, and the existing Transformer model, which excels in time-series predictions, cannot be directly applied to forecast the valve overtemperature state. In this study, we modified the Transformer and present a hybrid Transformer–FCM–NN (TransFNN) model to predict the future overtemperature state of the converter valve. The TransFNN model decouples the forecast process into two stages: (i) The modified Transformer is used to obtain the future values of the independent parameters; (ii) the relation between the valve cooling water temperature and the six independent operating parameters is fit, and the output of the Transformer is used to calculate the future values of the cooling water temperature. The results of the quantitative experiments showed that the proposed TransFNN model outperformed other models with which it was compared; with TransFNN being applied to predict the overtemperature state of the converter valves, the forecast accuracy was 91.81%, which was improved by 6.85% compared with that of the original Transformer model. Our work provides a novel approach to predicting the valve overtemperature state and acts as a data-driven tool for operation and maintenance personnel to use to adjust valve cooling measures punctually, effectively, and economically. [ABSTRACT FROM AUTHOR]

Deep learning effectively identifies and predicts modes but faces performance reduction under few-shot learning conditions. In this paper, a time series prediction framework for small samples is proposed, including a data augmentation algorithm, time series trend decomposition, multi-model prediction, and error-based fusion. First, data samples are augmented by retaining and extracting time series features. Second, the expanded data are decomposed based on data trends, and then, multiple deep models are used for prediction. Third, the models' predictive outputs are combined with an error estimate from the intersection of covariances. Finally, the method is verified using natural systems and classic small-scale simulation datasets. The results show that the proposed method can improve the prediction accuracy of small sample sets with data augmentation and multi-model fusion. [ABSTRACT FROM AUTHOR]

Future trends in climate change, water scarcity, and energy costs will motivate agriculturists to develop innovative agricultural systems. In order to achieve sustainable farming in arid regions, there is an urgent need to use artificial intelligence (AI) to predict and estimate the optimum water and energy requirements for the irrigation of date palms. Therefore, this study aimed to predict the optimum water and energy requirements for date palm irrigation depending on the optimum water use efficiency (WUE) and yield in arid conditions. To achieve this aim, four solar-powered micro irrigation systems were developed and evaluated under six irrigation levels for date palm irrigation. Soil moisture sensor-based controllers were used to automate irrigation scheduling for the micro irrigation systems. The water pumping in these systems was powered using a solar photovoltaic (PV) system. In addition, four machine-learning (ML) algorithms, including linear regression (LR), support vector regression (SVR), long short-term memory (LSTM) neural network, and extreme gradient boosting (XGBoost), were developed and validated for prediction purposes. These models were developed in Python programing language using the Keras library. The results indicated that the optimum WUS was achieved when the maximum setpoints of irrigation control were adjusted at the field capacity and by adjusting the minimum setpoints at 40, 50, 70, and 80% of the available water (AW). The optimum yield was achieved by adjusting the minimum setpoints at 60, 70, 80, and 90% of AW for subsurface irrigation, subsurface drip irrigation, drip irrigation, and bubbler irrigation, respectively. Therefore, the dataset was prepared at these levels for four years to train and test the models, and a fifth year was used to validate the performance of the best model. The evaluation of the models showed that the LSTM followed by XGBoost models were more accurate than the SVR and LR models for predicting the optimum irrigation water and energy requirements. The validation result showed that the LSTM was able to predict the water and energy requirements for all irrigation systems with R2 ranging from 0.90 to 0.92 based on limited meteorological variables and date palm age. The findings of the current study demonstrated that the developed LSTM model can be a powerful tool in irrigation water and energy management as a fast and easy-to-use approach. [ABSTRACT FROM AUTHOR]

Prediction with liigh reliability is very important in solving real-world problems, especially those that affect public health. The statistical properties of complex problems such as Covid-19 disease constantly change over time which makes modeling of such problems associated with high-level uncertainty. It has been proven that the type-2 fuzzy logic has the potential for modeling uncertainty to solve complex problems. In this research, for the first time, an intelligent method based on the capability of type-2 fuzzy logic was presented to manage uncertainty in predicting short-term and long-term time series in environmental crises such as the Covid-19 pandemic. The performance of the proposed model was evaluated using a real dataset collected from official sources. The results confirm the high efficiency of the proposed method on Covid-19 based on a ROC curve analysis. The obtained results showed an efficiency of 93.81٥/٠ for short and 91.33٥/٠ for long-term time series. This indicates the liigh efficiency and capability of the proposed model for managing uncertainty in predicting patterns of Covid-19 in comparison with similar methods. The proposed model can be useful to take strategic decisions and prevent the consequences of the Covid-19 epidemic in the short and long terms. [ABSTRACT FROM AUTHOR]

Studying the dynamics of plant populations is crucial to the conservation and management of endangered plants. The diameter at breast height (DBH), tree height, crown width, and environmental factors of a total of 15 plots in five populations in Guizhou Province, China were measured in this study. The method of substituting diameter at breast height for age was used to divide the population into 7 age classes. The age structure, static life table, and relationship between the population characteristics and environmental factors of the Litsea coreana Levl. var. sinensis population were studied. Moreover, the time series model was used to predict the population dynamics. Results showed that the age structure of population in Zhengan County (Population ZA) had an inverted J-shape with abundant seedlings and insufficient middle-aged and elderly individuals. The age structures of populations in Daozhen County (Population DZ), Kaiyang County (KY), Meitan County (Population MT), and Xishui County (Population XS) were spindle-shaped with few young individuals and many middle-aged and elderly individuals. The mortality rate of each population fluctuated with the increase in age class, and the populations were extremely unstable. The survival curve was close to the Deevey III type, and the degree of deviation was related to the lack of young individuals. After the age stages II, IV, and VI, the number of aged plants in the different populations initially increased then decreased, and population stability was difficult to maintain. Redundancy analysis showed that the DBH, tree height and crown width of the population decreased with the increase of annual mean temperature, and the population density was restricted by soil nutrient conditions to a certain extent. The results of this work provide scientific basis for the conservation and sustainable utilization of natural populations of L. coreana var. sinensis in Guizhou Province, China. [ABSTRACT FROM AUTHOR]