Prediction Process Research Articles

Spatial-temporal memory enhanced multi-level attention network for origin-destination demand prediction

Origin-destination demand prediction is a critical task in the field of intelligent transportation systems. However, accurately modeling the complex spatial-temporal dependencies presents significant challenges, which arises from various factors, including spatial, temporal, and external influences such as geographical features, weather conditions, and traffic incidents. Moreover, capturing multi-scale dependencies of local and global spatial dependencies, as well as short and long-term temporal dependencies, further complicates the task. To address these challenges, a novel framework called the Spatial-Temporal Memory Enhanced Multi-Level Attention Network (ST-MEN) is proposed. The framework consists of several key components. Firstly, an external attention mechanism is incorporated to efficiently process external factors into the prediction process. Secondly, a dynamic spatial feature extraction module is designed that effectively captures the spatial dependencies among nodes. By incorporating two skip-connections, this module preserves the original node information while aggregating information from other nodes. Finally, a temporal feature extraction module is proposed that captures both continuous and discrete temporal dependencies using a hierarchical memory network. In addition, multi-scale features cascade fusion is incorporated to enhance the performance of the proposed model. To evaluate the effectiveness of the proposed model, extensively experiments are conducted on two real-world datasets. The experimental results demonstrate that the ST-MEN model achieves excellent prediction accuracy, where the maximum improvement can reach to 19.1%.

Predicting Oil Production After Enhancement Techniques Using Multidimensional Feature Representation Learning: A Case Study of Profile Control Technique

Summary The prediction of oil production following enhancement techniques has garnered widespread attention, leading scientists to explore this area using machine learning. However, field data collection constraints and single model accuracy limitations mean few models can precisely predict daily oil production after technique implementation. Building upon previous research, this paper introduces a model that predicts oil production after enhancement operations, utilizing multidimensional feature representation learning. It thoroughly examines three characteristic categories affecting the effectiveness of oil production enhancement techniques: geological static parameters, production dynamic parameters, and enhancement technique process parameters. The model comprehensively explores these features with an emphasis on global spatial, local spatial, and temporal information. A complete machine learning prediction process is established, which includes data preprocessing, model training, cross-validation, and oil production prediction after implementing enhancement techniques. The first part of the model involves representation learning on processed data, producing three sets of new features: global spatial, local spatial, and temporal information. These features are fused with the original data, serving as input for the advanced ensemble learning model XGBoost, which predicts daily oil production after implementing the technique. Following the construction of the model, actual field data from profile control techniques are selected to conduct various evaluations based on the model’s performance on validation and test sets. Compared with traditional machine learning regression algorithms, this model demonstrates significantly higher predictive accuracy. The prediction accuracy for oil production using given enhanced techniques reached 96% in the validation set and 94% in the test set. This research provides a technical foundation for selecting appropriate production enhancement techniques in oil fields by accurately predicting oil production after implementing enhancement techniques, which offers guidance for actual oilfield production.

Pattern-Based Recommender System Using Nuclear Norm Minimization of Three-Mode Tensor and Quantum Fidelity-Based K-Means

Recommender systems (RSs) consist of predicting missing ratings based on the observed ones. This problem corresponds to matrix completion where users are its rows and items are its columns and it contains the observed ratings. An efficient RS is the one promoting the personal relevancy of its users which is not the case in the matrix completion process. It takes into account all the rates for prediction without including the users’ and items’ characteristics. Patterns are the key enablers of such solutions. In this work, we present a three-mode tensor representation with two aspects namely user–item interactions (observed ratings) and item–item relationship (detected similarities). To capture the similarities, the patterns are grouped in an equivalent manner using a bi-quantum clustering process (Quantum K-means). This step is adopted to consider only the relevant observed ratings in the prediction process and express item-to-item relationship using fidelity distance. Then, for each missing rating r that a user u might give to an item i in the future, a sub-tensor is created according to the detected patterns. This sub-tensor then is completed by minimizing its rank. This problem is NP-hard, hence a surrogate is used which is the nuclear norm. The effectiveness of the proposed approach is measured according to information retrieval evaluation criteria: precision, recall and [Formula: see text]-measure. The proposed approach improved the precision of the state-of-the-art methods by 20[Formula: see text].

Convolutional Neural Network and LSTM for Seat Belt Detection in Vehicles using YOLO3

The application of an electronic violation detection system has begun to be implemented in many countries using CCTV cameras installed at highway and toll road points. However, the development of a violation detection system using data in the form of images that have a high level of accuracy is still a challenge for researchers. Several types of violations detected include the use of seat belts, the use of cell phones while driving, which is influenced by the number of vehicles, vehicle speed and lighting, which can increase the difficulty in the detection process. This research developed a traffic violation detection system using a hybrid model, namely the CNN and LSTM algorithms for the application of discipline using seat belts. The dataset was obtained from RoboFlow Universe with a total of 199 front view car images consists of 82 using seatbelts and 78 not using seatbelts for the training process. The CNN algorithm plays a role in the feature extraction process from input image data, while the LSTM algorithm plays a role in the prediction process. Additionally, the performance evaluation of the CNN+LSTM algorithm will be measured using the accuracy value to measure the performance of the training process and testing process. When measuring the performance of the training process, it will be compared with several basic detection models used, such as CNN, VGG16, ResNet50, MobileNetV2, Yolo3, Yolo3+LSTM. The test results show that Yolo3+LSTM has a higher accuracy compared to the others, at 89%. Next, in the testing process, the CNN+LSTM model will be compared with the basic method, namely CNN. The test results show that the CNN+LSTM models have a higher accuracy of 89%. Meanwhile, in the basic CNN model, the resulting accuracy was 85%.

A novel evidence reasoning-based RUL prediction method integrating uncertainty information

The remaining useful life (RUL) prediction plays an essential role in maintenance decision making to ensure safe operation of mechanical equipment. However, it still remains challenges to mine and utilize the uncertainty information hidden behind the running data and to enhance the interpretability of the prediction process, which leads to inaccurate and unreasonable prediction results. Therefore, a novel evidence reasoning-based RUL prediction method integrating uncertainty information is proposed to overcome these limitations. First, a sample casting method is presented to transform the historical sample set of an input feature sequence into the corresponding referential life evidence matrix (RLEM), achieving almost lossless transformation from RUL about the input feature sequence to life evidence. Second, the real-time samples of multiple input features activate their corresponding RLEMs to obtain the activated life evidence, and use evidence reasoning (ER) rule to fuse these activated life evidence to ultimately obtain integrated life evidence. Then, to further improve the accuracy of integrated life evidence, the reliability of activated life evidence is calculated online through sample entropy method, and the weight of life evidence is optimized offline using genetic algorithm. Finally, the integrated life evidence is transformed into predicted values of RUL based on utility theory. Two case studies on rolling bearing and hydrogen fuel cell show the superiority of the proposed RUL prediction method.

OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs.

Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation. We present a novel approach to simultaneously predict and optimize ligand bioactivities through DGL and ACs (OLB-AC). OLB-AC possesses the capability to optimize ligand molecules located near ACs, providing a direct reference for optimizing ligand bioactivities with the matching of original ligands. To accomplish this, a novel attentive graph reconstruction neural network and ligand optimization scheme are proposed. Attentive graph reconstruction neural network reconstructs original ligands and optimizes them through adversarial representations derived from their bioactivity prediction process. Experimental results on nine drug targets reveal that out of the 667 molecules generated through OLB-AC optimization on datasets comprising 974 low-activity, noninhibitor, or highly toxic ligands, 49 are recognized as known highly active, inhibitor, or nontoxic ligands beyond the datasets' scope. The 27 out of 49 matched molecular pairs generated by OLB-AC reveal novel transformations not present in their training sets. The adversarial representations employed for ligand optimization originate from the gradients of bioactivity predictions. Therefore, we also assess OLB-AC's prediction accuracy across 33 different bioactivity datasets. Results show that OLB-AC achieves the best Pearson correlation coefficient (r2) on 27/33 datasets, with an average improvement of 7.2%-22.9% against the state-of-the-art bioactivity prediction methods. The code and dataset developed in this work are available at github.com/Yueming-Yin/OLB-AC.

PM2.5 concentration prediction based on EEMD-ALSTM

The concentration prediction of PM2.5 plays a vital role in controlling the air and improving the environment. This paper proposes a prediction model (namely EEMD-ALSTM) based on Ensemble Empirical Mode Decomposition (EEMD), Attention Mechanism and Long Short-Term Memory network (LSTM). Through the combination of decomposition and LSTM, attention mechanism is introduced to realize the prediction of PM2.5 concentration. The advantage of EEMD-ALSTM model is that it decomposes and combines the original data using the method of ensemble empirical mode decomposition, reduces the high nonlinearity of the original data, and Specially reintroduction the attention mechanism, which enhances the extraction and retention of data features by the model. Through experimental comparison, it was found that the EEMD-ALSTM model reduced its MAE and RMSE by about 15% while maintaining the same R2 correlation coefficient, and the stability of the model in the prediction process was also improved significantly.

High-precision air conditioning load forecasting model based on improved sparrow search algorithm

Air conditioning load prediction is a crucial prerequisite for ensuring the efficient operation of air conditioning systems and enhancing demand-side response in electricity. Due to the uncertainty in model parameter settings and limitations of the Sparrow Search Algorithm (SSA2) itself, leading to significant human and material resources consumption during the prediction process, this study improves SSA (ISSA) to enhance air conditioning prediction efficiency and accuracy. The enhanced SSA is then combined with Long Short-Term Memory (LSTM). Firstly, Singular Spectrum Analysis (SSA1) is employed for feature extraction on the load to obtain more regular sub-signals, facilitating subsequent prediction tasks. Simultaneously, the Maximum Information Coefficient (MIC) is used to measure the correlation between data, assisting in selecting input variables for the model. Subsequently, each sub-signal is input into the ISSA-LSTM model for prediction, and the prediction results of each sub-signal are reconstructed to obtain the final prediction. Finally, data from air conditioning loads in two different types of buildings are collected to validate the model. Upon validation, it was found that the SSA1-ISSA- LSTM model exhibits superior performance compared to other advanced combination models. When predicting air conditioning loads in office building, the R2 value reaches 0.9955, with MAPE, MAE, and RMSE being 0.73 %, 4.658RT, and 6.1151RT respectively. When predicting air conditioning loads in medical building, the R2 is as high as 0.9920, with MAPE, MAE, and RMSE being 0.55 %, 5.8543RT, and 7.9599RT respectively.

Research on surface modification evaluation on composites interfacial properties

Abstract At present, composites are formed by multi-phase materials with reinforcement and matrix, which can produce a discontinuous interface layer between two phases. Therefore, surface modification methods are proposed to enhance the performance and the composite properties. However, the evaluation of these properties primarily relies on manual detection and this may cause mistakes in some situations. In this work, an automatic evaluation model is proposed to achieve the interfacial properties estimation. The neural network consists of multiple layers of neural cells and the evaluation is simulated by the prediction process with enough training iterations. Initially, the neural network model is trained with tremendous composite properties with different surface modification methods and the evaluation results are labelled. Further, the inner parameters are set and the convergence condition is controlled to obtain an acceptable evaluation model. Finally, our model is compared with existing evaluation methods. From our extensive experiments and analysis, it can be concluded that our model can realize the evaluation for the performance of surface modification in interfacial properties.

GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling

BackgroundProtein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance.ResultsIn this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set.ConclusionsGATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol.

SurvNet: A low-complexity convolutional neural network for survival time classification of patients with glioblastoma

Background and objectiveMalignant primary brain tumors cause the greatest number of years of life lost than any other cancer. Grade 4 glioma is particularly devastating: The median survival without any treatment is less than six months and with standard-of-care treatment is only 14.6 months. Accurate identification of the overall survival time of patients with brain tumors is of profound importance in many clinical applications. Automated image analytics with magnetic resonance imaging (MRI) can provide insights into the prognosis of patients with brain tumors. MethodsIn this paper, We propose SurvNet, a low-complexity deep learning architecture based on the convolutional neural network to classify the overall survival time of patients with brain tumors into long-time and short-time survival cohorts. Through the incorporation of diverse MRI modalities as inputs, we facilitate deep feature extraction at various anatomical sites, thereby augmenting the precision of predictive modeling. We compare SurvNet with the Inception V3, VGG 16 and ensemble CNN models on pre-operative magnetic resonance image datasets. We also analyzed the effect of segmented brain tumors and training data on the system performance. ResultsSeveral measures, such as accuracy, precision, and recall, are calculated to examine the perfor-mance of SurvNet on three-fold cross-validation. SurvNet with T1 MRI modality achieved a 62.7 % accuracy, compared with 52.9 % accuracy of the Inception V3 model, 58.5 % accuracy of the VGG 16 model, and 54.9 % of the ensemble CNN model. By increasing the MRI input modalities, SurvNet becomes more accurate and achieves 76.5 % accuracy with four MRI modalities. Combining the segmented data, SurvNet achieved the highest accuracy of 82.4 %. ConclusionsThe research results show that SurvNet achieves higher metrics such as accuracy and f1-score than the comparisons. Our research also proves that by using multiparametric MRI modalities, SurvNet is able to learn more image features and performs a better classification accuracy. We can conclude that SurvNet with the complete scenario, i.e., segmented data and four MRI modalities, achieved the best accuracy, showing the validity of segmentation information during the survival time prediction process.

Revolutionizing Disease Prediction with Deep Learning and Predictive Analysis

Abstract: The integration of deep learning and predictive analysis has emerged as a powerful tool in the healthcare improvement effort, revolutionizing the process of illness prediction. The purpose of this project is to develop a comprehensive framework that can precisely predict and identify a range of diseases at an early stage using these technologies. Deep learning algorithms are used to uncover complicated patterns and connections that may point to the onset of a disease by evaluating massive datasets that include patient demographics, medical history, genetic data, and environmental factors. The predictive capabilities of these algorithms enable healthcare practitioners to more precisely identify potential health issues, enabling preventive intervention and customized treatment regimens. Moreover, putting this idea into reality promises to improve patient outcomes, substantially reduce healthcare costs, and ultimately alter the landscape of preventative medicine.

An interpretable online prediction method for remaining useful life of lithium-ion batteries

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is advantageous for maintaining the stability of electrical systems. In this paper, an interpretable online method which can reflect capacity regeneration is proposed to accurately estimate the RUL. Firstly, four health indicators (HIs) are extracted from the charging and discharging process for online prediction. Then, the HIs model is trained using support vector regression to obtain future features. And the capacity model of Gaussian process regression (GPR) is trained and analyzed by Shapley additive explanation (SHAP). Meanwhile, the state space for capacity prediction is constructed with the addition of Gaussian non-white noise to simulate the capacity regeneration. And the modified predicted HIs and noise are obtained by unscented Kalman filter. Finally, according to SHAP explainer, the predicted HIs acting as the baseline and the modified HIs containing information on capacity regeneration are chosen to predict RUL. In addition, the bounds of confidence intervals (CIs) are calculated separately to reflect the regenerated capacity. The experimental results demonstrate that the proposed online method can achieve high accuracy and effectively capture the capacity regeneration. The absolute error of failure RUL is below 5 and the minimum confidence interval is only 2.

An improved convolutional neural network for predicting porous media permeability from rock thin sections

Permeability prediction is a crucial aspect of investigating fluid flow capacity in porous media, and rapidly predicting the permeability of rock thin sections aids in reservoir assessment. In recent years, significant progress has been made regarding the use of deep learning for predicting porous media permeability, effectively overcoming the limitations associated with time-consuming conventional permeability determination methods. To address the issues of low prediction accuracy and insufficient generalization capability of previous permeability prediction models, this study proposes an improved convolutional neural network for porous media permeability and applies it to predict the permeability of rock thin sections. Given the difficulty of obtaining a large amount of rock thin section data, we synthetically generated 1000 binary images of porous media and calculated the corresponding permeability labels using computational fluid dynamics. These data were used as foundational data for training the model. The proposed model achieved accurate and efficient permeability prediction of porous media with a correlation of 0.9667 on testing set. Simultaneously, we employed class activation mapping technology to visualize the permeability prediction process of the neural network, thereby elucidating the working principles of the neural network during porous media permeability prediction. Subsequently, based on an optimally pretrained neural network, transfer learning was utilized to predict the permeability of rock thin sections, which achieved a correlation of 0.9032. Our study extends the rock thin section applications and provides technical support for reservoir evaluation.

A Review on Classification Algorithm for Customer Churn Classification

Any sector faces a huge obstacle when it comes to retaining existing customers. The percentage of consumers who have quit using a product or service is referred to as customer churn, and it is a vital indication that offers reliable information about this percentage. When it comes to achieving long-term success in a market or industry, one of the most significant challenges that any company must face is the ability to keep their precious clients and to fulfill their needs. A review of the most significant studies on Customer Churn Prediction is presented in this paper so as to furnish the reader with an overview of frequently employed data mining methodologies and their respective performances. We provide the available statistics in addition to customer information in order to approximate customer attrition. The time period encompassing the survey extends from 2003 to 2023. During the process of Customer Churn Prediction, we identified the issues and difficulties that were linked with it and offered guidance and potential remedies.

Use of explainable machine learning models in blast load prediction

The effects of blast waves and their consequent damage to structures have been an increasingly popular research topic in the past decade. Various methods are used in blast load prediction on structures, including experimental, semi-empirical and numerical approaches. However, there is a demand for developing time-efficient predictive methods that help various professionals, such as engineering practitioners and first responders, in their regular activities. Machine learning (ML), as a subset of artificial intelligence (AI), has gained significant attention in various engineering applications over the past decades for its ability to aid decision-making. However, a critical gap exists in the comprehensive analysis of these ML models' transparency and interpretability levels. This paper presents a novel approach to employing explainable machine learning (XML) to predict the blast loads generated by high explosives. The primary objective is to assess the validity of utilising a machine learning (ML) framework for blast load predictions, examining the scientific consistency of the model's prediction process. This research utilises XML to interpret the ML model-based blast load prediction framework, marking the first documented use of XML in published literature for blast load prediction. This research study utilised three ML models, namely a) Decision Tree, b) Random Forest, and c) Extreme Gradient Boost (XGB) models, to predict blast loads on rigid structures. Validated numerical model results were used to generate the dataset to train the ML models, and the respective dataset consists of approximately 600 independent blast events. The models were evaluated for their predictability using error metrics: adjusted coefficient of determination (adjusted R2), root mean square error (RMSE), and mean absolute error (MAE). The overall ML model performance evaluation showed that the best-performing model, XGB, could make predictions with 98 % accuracy compared to validated numerical predictions. The XML explanations for the XGB models showcased that the blast load prediction procedure of the models aligns with the blast physics principles, ensuring the credibility of the developed ML models. In summary, these XML predictive models can be considered accurate and reliable for blast load prediction on structures.

Proposing an inherently interpretable machine learning model for shear strength prediction of reinforced concrete beams with stirrups

Advanced machine learning (ML) models are utilized for accurate shear strength prediction of reinforced concrete beams (RCB), but their lack of interpretability makes it unclear how models make specific predictions, reducing their reliability and applicability. Mainstream model-agnostic interpretation methods require numerous additional computing procedures, limiting the interpretation efficiency, and the model prediction process remains unknown. This study proposes an inherently interpretable shear strength prediction model for RCB with stirrups based on Explainable Boosting Machine (EBM) and Bayesian optimization. The EBM algorithm decomposes the predicted shear strength into individual shape functions, thus thoroughly revealing the prediction process. Besides, the ML tasks of selecting a model with optimal hyperparameters are automatically performed by Bayesian optimization to reduce computational cost. The developed model is validated on a database including 372 specimens. Compared with shear design codes, empirical models and prominent ML models, the EBM model obtains most accurate predictions for the test set with R2, MAE, and RMSE of 0.9293, 35.42 kN, and 50.99 kN, respectively. The accuracy of EBM is robust to varying input variables through trend analysis. Its interpretability fully discloses the contribution of individual features to the shear strength, and the model rationality is verified by comparing feature contribution with existing mechanisms. The proposed EBM model achieves inherently interpretable shear strength predictions while maintaining high accuracy, which promotes the model applicability in structural assessment.

Prediction of nanocomposite properties and process optimization using persistent homology and machine learning

Physical property prediction and synthesis process optimization are key targets in material informatics. In this study, we propose a machine learning approach that utilizes ridge regression to predict the oxygen permeability at fuel cell electrode surfaces and determine the optimal process temperature. These predictions are based on a persistence diagram derived from tomographic images captured using transmission electron microscopy (TEM). Through machine learning analysis of the complex structures present in the Pt/CeO2 nanocomposites, we discovered that l2 regularization considering diverse structural elements is more appropriate than l1 regularization (sparse modeling). Notably, our model successfully captured the activation energy of oxygen permeability, a phenomenon that could not be solely explained by the geometric feature of the Betti numbers, as demonstrated in a previous study. The correspondence between the ridge regression coefficient and persistence diagram revealed the formation process of the local and three-dimensional structures of CeO2 and their contributions to pre-exponential factor and activation energies. This analysis facilitated the determination of the annealing temperature required to achieve the optimal structure and accurately predict the physical properties.

A quality‐related distributed process monitoring framework for large‐scale manufacturing processes with multirate sampling measurements

SummaryQuality‐related process monitoring has become a research hot‐spot in the field of industrial control because it is essential to ensure the process safety and product quality. A great number of data driven quality‐related process monitoring methods have been developed for large‐scale manufacturing processes, and most of them are developed based on homogeneous sampling measurement. Therefore, it is necessary to develop quality‐related monitoring methods for large‐scale processes with multirate sampling measurements. In this paper, a new quality‐related distributed monitoring framework for large‐scale manufacturing processes with multirate sampling measurements is proposed. First, a new subsystem decomposition method for multirate sampling processes combining prior knowledge and mutual information is proposed by introducing mathematic expectations. Second, local monitoring model is designed for each subsystem. Multirate partial least squares regression is adopted for modeling among the process and quality variables. The monitoring metrics of the isolation‐based anomaly detection using nearest‐neighbor ensembles are built in prediction space and process space, respectively. Finally, Bayesian inference is introduced to obtain statistical indicators for the plant‐wide processes. The validity of the proposed framework is verified in Tennessee Eastman process and a real hot strip mill process. The results show that the proposed method has favorable effectiveness and significant performance gains compared with state‐of‐the‐art methods.

State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China

The use of renewable energy sources, such as wind power, has received more attention in China, and wind turbine system reliability has become more important. Based on existing research, this study proposes a state reliability prediction model for wind turbine systems based on XGBoost–LSTM. By considering the dynamic variability of the weight fused by the algorithm, under the irregular fluctuation of the same parameter with time in nonlinear systems, it reduces the algorithm defects in the prediction process. The improved algorithm is validated by arithmetic examples, and the results show that the root mean square error value (hereinafter abbreviated as RMSE) and the mean absolute error value (hereinafter abbreviated as MAPE) of the improved XGBoost–LSTM algorithm are decreased compared with those for the LSTM and XGBoost algorithms, among which the RMSE is reduced by 8.26% and 4.15% and the MAPE is reduced by 24.56% and 27.99%, respectively; its goodness-of-fit R2 value is closer to 1. This indicates that the algorithm proposed in this paper reduces the existing defects present in some current algorithms, and the prediction accuracy is effectively improved, which is of great value in improving the reliability of the system.