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Artículos de revistas sobre el tema "Periodic prediction"

Autor: Grafiati

Publicado: 1 de junio de 2024

Última modificación: 31 de julio de 2025

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1

Niu, Xiaoxu, Junwei Ma, Yankun Wang, Junrong Zhang, Hongjie Chen, and Huiming Tang. "A Novel Decomposition-Ensemble Learning Model Based on Ensemble Empirical Mode Decomposition and Recurrent Neural Network for Landslide Displacement Prediction." Applied Sciences 11, no. 10 (2021): 4684. http://dx.doi.org/10.3390/app11104684.

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As vital comments on landslide early warning systems, accurate and reliable displacement prediction is essential and of significant importance for landslide mitigation. However, obtaining the desired prediction accuracy remains highly difficult and challenging due to the complex nonlinear characteristics of landslide monitoring data. Based on the principle of “decomposition and ensemble”, a three-step decomposition-ensemble learning model integrating ensemble empirical mode decomposition (EEMD) and a recurrent neural network (RNN) was proposed for landslide displacement prediction. EEMD and ku

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2

Yang, Xiaoxue, Yajie Zou, Jinjun Tang, Jian Liang, and Muhammad Ijaz. "Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models." Journal of Advanced Transportation 2020 (January 20, 2020): 1–16. http://dx.doi.org/10.1155/2020/9628957.

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Accurate prediction of traffic information (i.e., traffic flow, travel time, traffic speed, etc.) is a key component of Intelligent Transportation System (ITS). Traffic speed is an important indicator to evaluate traffic efficiency. Up to date, although a few studies have considered the periodic feature in traffic prediction, very few studies comprehensively evaluate the impact of periodic component on statistical and machine learning prediction models. This paper selects several representative statistical models and machine learning models to analyze the influence of periodic component on sho

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3

Ren, Liang, Feng Yang, Yuanhe Gao, and Yongcong He. "Predicting Spacecraft Telemetry Data by Using Grey–Markov Model with Sliding Window and Particle Swarm Optimization." Journal of Mathematics 2023 (February 3, 2023): 1–14. http://dx.doi.org/10.1155/2023/9693047.

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Predicting telemetry data is vital for the proper operation of orbiting spacecraft. The Grey–Markov model with sliding window (GMSW) combines Grey model (GM (1, 1)) and Markov chain forecast model, which allows it to describe the fluctuation of telemetry data. However, the Grey–Markov model with sliding window does not provide better predictions of telemetry series with the pseudo-periodic phenomenon. To overcome this drawback, we improved the GMSW model by applying particle swarm optimization (PSO) algorithm a sliding window for better prediction of spacecraft telemetry data (denoted as PGMSW

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4

Lin, Hui-Ting Christine, and Vincent S. Tseng. "Periodic Transformer Encoder for Multi-Horizon Travel Time Prediction." Electronics 13, no. 11 (2024): 2094. http://dx.doi.org/10.3390/electronics13112094.

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In the domain of Intelligent Transportation Systems (ITS), ensuring reliable travel time predictions is crucial for enhancing the efficiency of transportation management systems and supporting long-term planning. Recent advancements in deep learning have demonstrated the ability to effectively leverage large datasets for accurate travel time predictions. These innovations are particularly vital as they address both short-term and long-term travel demands, which are essential for effective traffic management and scheduled routing planning. Despite advances in deep learning applications for traf

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5

Sugimoto, Masashi, Naoya Iwamoto, Robert W. Johnston, Keizo Kanazawa, Yukinori Misaki, and Kentarou Kurashige. "A Study of Predicting Ability in State-Action Pair Prediction." International Journal of Artificial Life Research 7, no. 1 (2017): 52–66. http://dx.doi.org/10.4018/ijalr.2017010104.

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When a robot considers an action-decision based on a future prediction, it is necessary to know the property of disturbance signals from the outside environment. On the other hand, the properties of disturbance signals cannot be described simply, such as non-periodic function, nonlinear time-varying function nor almost-periodic function. In case of a robot control, sampling rate for control will be affected description of disturbance signals such as frequency or amplitude. If the sampling rate for acquiring a disturbance signal is not correct, the action will be taken far from its actual prope

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6

Shen, Yueqian, Xiaoxia Ma, Yajing Sun, and Sheng Du. "Prediction of university fund revenue and expenditure based on fuzzy time series with a periodic factor." PLOS ONE 18, no. 5 (2023): e0286325. http://dx.doi.org/10.1371/journal.pone.0286325.

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Financial management and decision-making of universities play an essential role in their development. Predicting fund revenue and expenditure of universities can provide a necessary basis for funds risk prevention. For the lack of solid data reference for financial management and funds risk prevention in colleges and universities, this paper presents a prediction model of University fund revenue and expenditure based on fuzzy time series with a periodic factor. Combined with the fuzzy time series, this prediction method introduces the periodic factor of university funds. The periodic factor is

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7

Cheng, Weiwei, Guigen Nie, and Jian Zhu. "Characterizing Periodic Variations of Atomic Frequency Standards via Their Frequency Stability Estimates." Sensors 23, no. 11 (2023): 5356. http://dx.doi.org/10.3390/s23115356.

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The onboard atomic frequency standard (AFS) is a crucial element of Global Navigation Satellite System (GNSS) satellites. However, it is widely accepted that periodic variations can influence the onboard AFS. The presence of non-stationary random processes in AFS signals can lead to inaccurate separation of the periodic and stochastic components of satellite AFS clock data when using least squares and Fourier transform methods. In this paper, we characterize the periodic variations of AFS using Allan and Hadamard variances and demonstrate that the Allan and Hadamard variances of the periodics

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8

Miao, Xu, Bing Wu, Yajie Zou, and Lingtao Wu. "Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches." Journal of Advanced Transportation 2020 (August 1, 2020): 1–15. http://dx.doi.org/10.1155/2020/3463287.

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Freeway travel time prediction is a key technology of Intelligent Transportation Systems (ITS). Many scholars have found that periodic function plays a positive role in improving the prediction accuracy of travel time prediction models. However, very few studies have comprehensively evaluated the impacts of different periodic functions on statistical and machine learning models. In this paper, our primary objective is to evaluate the performance of the six commonly used multistep ahead travel time prediction models (three statistical models and three machine learning models). In addition, we c

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9

Scerri, Eric R., and John Worrall. "Prediction and the periodic table." Studies in History and Philosophy of Science Part A 32, no. 3 (2001): 407–52. http://dx.doi.org/10.1016/s0039-3681(01)00023-1.

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10

Pawelzik, K., and H. G. Schuster. "Unstable periodic orbits and prediction." Physical Review A 43, no. 4 (1991): 1808–12. http://dx.doi.org/10.1103/physreva.43.1808.

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11

Zhao, Lin, Nan Li, Hui Li, Renlong Wang, and Menghao Li. "BDS Satellite Clock Prediction Considering Periodic Variations." Remote Sensing 13, no. 20 (2021): 4058. http://dx.doi.org/10.3390/rs13204058.

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The periodic noise exists in BeiDou navigation satellite system (BDS) clock offsets. As a commonly used satellite clock prediction model, the spectral analysis model (SAM) typically detects and identifies the periodic terms by the Fast Fourier transform (FFT) according to long-term clock offset series. The FFT makes an aggregate assessment in frequency domain but cannot characterize the periodic noise in a time domain. Due to space environment changes, temperature variations, and various disturbances, the periodic noise is time-varying, and the spectral peaks vary over time, which will affect

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12

Jiang, Shan, Yuming Feng, Xiaofeng Liao, Hongjuan Wu, Jinkui Liu, and Babatunde Oluwaseun Onasanya. "A Novel Spatiotemporal Periodic Polynomial Model for Predicting Road Traffic Speed." Symmetry 16, no. 5 (2024): 537. http://dx.doi.org/10.3390/sym16050537.

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Accurate and fast traffic prediction is the data-based foundation for achieving traffic control and management, and the accuracy of prediction results will directly affect the effectiveness of traffic control and management. This paper proposes a new spatiotemporal periodic polynomial model for road traffic, which integrates the temporal, spatial, and periodic features of speed time series and can effectively handle the nonlinear mapping relationship from input to output. In terms of the model, we establish a road traffic speed prediction model based on polynomial regression. In terms of spati

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13

Zhang, Junrong, Huiming Tang, Dwayne D. Tannant, et al. "A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm." Sensors 21, no. 24 (2021): 8352. http://dx.doi.org/10.3390/s21248352.

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With the widespread application of machine learning methods, the continuous improvement of forecast accuracy has become an important task, which is especially crucial for landslide displacement predictions. This study aimed to propose a novel prediction model to improve accuracy in landslide prediction, based on the combination of multiple new algorithms. The proposed new method includes three parts: data preparation, multi-swarm intelligence (MSI) optimization, and displacement prediction. In the data preparation, the complete ensemble empirical mode decomposition (CEEMD) is adopted to separa

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14

Li, Hongcheng, Yuan Gao, Bing Wang, Yuewei Ming, and Zhongwei Zhao. "Network Anomaly Sequence Prediction Method Based on LSTM and Two-layer Window Features." Journal of Physics: Conference Series 2216, no. 1 (2022): 012063. http://dx.doi.org/10.1088/1742-6596/2216/1/012063.

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Abstract To solve the over-fitting problem in the prediction algorithm caused by the small number of features that arise during the network anomaly prediction process, an LSTM algorithm for network anomaly predictions based on two-layer time window features was proposed. Firstly, the network alarm data sequence was divided according to the observation time window and prediction time window. Secondly, considering that the time series of the anomaly alarm data can be somewhat periodic, a time window sequence dataset was created with the periodic features and statistical features in the two-layer

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15

Bittanti, S., P. Colaneri, and G. De Nicolao. "The difference periodic Ricati equation for the periodic prediction problem." IEEE Transactions on Automatic Control 33, no. 8 (1988): 706–12. http://dx.doi.org/10.1109/9.1286.

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16

Park, SoYoung, HyeWon Lee, and Sungsu Lim. "Dynamic Periodic Event Graphs for multivariate time series pattern prediction." PeerJ Computer Science 11 (February 24, 2025): e2717. https://doi.org/10.7717/peerj-cs.2717.

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Understanding and predicting outcomes in complex real-world systems necessitates robust multivariate time series pattern analysis. Advanced techniques, such as dynamic graph neural networks, have shown significant efficacy for these tasks. However, existing approaches often overlook the inherent periodicity in data, leading to reduced pattern or event prediction accuracy, especially in periodic time series. We introduce a new method, called dynamic Periodic Event Graphs (PEGs), to tackle this challenge. The proposed method involves time series decomposition to extract seasonal components that

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17

Shen, Cong, Guocheng Wang, Lintao Liu, Dong Ren, Huiwen Hu, and Wenlong Sun. "Extraction of Periodic Terms in Satellite Clock Bias Based on Fourier Basis Pursuit Bandpass Filter." Remote Sensing 17, no. 5 (2025): 827. https://doi.org/10.3390/rs17050827.

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Effective noise management and control of periodic fluctuations in spaceborne atomic clocks are essential for the accuracy and reliability of Global Navigation Satellite Systems. Time-varying periodic terms can impact both the performance evaluation and prediction accuracy of satellite clocks, making it crucial to mitigate these influences in the clock bias. We propose methods based on the Fourier dictionary and basis pursuit, namely the Fourier basis pursuit (FBP) spectrum and the Fourier basis pursuit bandpass filter (FBPBPF), to analyze and extract periodic terms in the satellite clock bias

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18

Chen, Guisheng, and Zhanshan Li. "A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation." Entropy 23, no. 11 (2021): 1430. http://dx.doi.org/10.3390/e23111430.

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Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are e

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19

Chen, Tie, Shinan Guo, Zhifan Zhang, Yimin Yuan, and Jiaqi Gao. "A Method for Predicting Transformer Oil-Dissolved Gas Concentration Based on Multi-Window Stepwise Decomposition with HP-SSA-VMD-LSTM." Electronics 13, no. 14 (2024): 2881. http://dx.doi.org/10.3390/electronics13142881.

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Predicting the concentration of dissolved gases in transformer oil is a critical activity for the early detection of potential faults. To address the prevalent issue of data leakage in current prediction methods, this paper proposes a prediction method that completely avoids data leakage. First, the Hodrick Prescott (HP) filter is used for stepwise decomposition to obtain the long-term trend and high-frequency periodic component. The high-frequency periodic component is further decomposed using singular spectrum analysis (SSA) to extract periodic features. Dispersion entropy (DE) and fuzzy ent

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20

Lin, Yisha, Zongxiang Lu, Ying Qiao, Mingjie Li, and Zhifeng Liang. "Medium and long-term wind energy forecasting method considering multi-scale periodic pattern." E3S Web of Conferences 182 (2020): 01002. http://dx.doi.org/10.1051/e3sconf/202018201002.

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Medium and long-term weather sequence forecast becomes unreliable beyond two weeks since the weather is a chaotic system. Using values of same months for electricity prediction of wind power is the usual method. This approach defaults wind power output with annual cycle law. However, the periodic pattern can be very complicated in fact with multiple time scales. This paper proposes an approach with multi-scale periodic pattern considered. The application of parametric estimation on cumulative distribution function avoids the difficulty of predicting the power curve. Meteorological condition is

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21

Qian, Weiwe, Yuting Yang, Biao Yu, and Xuan Zhao. "Wind speed prediction based on deep learning and time frequency feature." Journal of Physics: Conference Series 3007, no. 1 (2025): 012050. https://doi.org/10.1088/1742-6596/3007/1/012050.

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Abstract Wind speed prediction is a crucial issue in meteorology and environmental science, and time-frequency features benefits the understand of various scales of wind variations and further boosting wind speed prediction performance. Therefore, this study designs a wind speed prediction network based on the Gated Recurrent Unit (GRU) network and Time Series Decomposition(TSD), and together with a scale attention mechanism to fuse features of different frequencies adaptively. Firstly, the observed data is decomposed into different periodic components through TSD, reducing the complexity of t

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22

Li, Yafei, Kejun Qian, Qiuying Shen, Qianli Ma, Xiaoliang Wang, and Zelin Wang. "CNN–Patch–Transformer-Based Temperature Prediction Model for Battery Energy Storage Systems." Energies 18, no. 12 (2025): 3095. https://doi.org/10.3390/en18123095.

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Accurate predictions of the temperature of battery energy storage systems (BESSs) are crucial for ensuring their efficient and safe operation. Effectively addressing both the long-term historical periodic features embedded within long look-back windows and the nuanced short-term trends indicated by shorter windows are key factors in enhancing prediction accuracy. In this paper, we propose a BESS temperature prediction model based on a convolutional neural network (CNN), patch embedding, and the Kolmogorov–Arnold network (KAN). Firstly, a CNN block was established to extract multi-scale periodi

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23

XU, Chuanbo, Maoru CHI, Liangcheng DAI, Yiping JIANG, Yongfa CHEN, and Zhaotuan GUO. "Research on Rubber Spring Model of High-speed EMU Based on Non-hyperelastic Forces." Mechanics 27, no. 1 (2021): 12–21. http://dx.doi.org/10.5755/j02.mech.25630.

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The research on the mechanical model of rubber spring is one of the hot spots in train dynamics. In order to accurately calculate the viscoelastic force of the rubber spring, especially the non-hyperelastic forces (NHEF) part, a NHEF model is proposed based on the elliptic approximation method. Furthermore, the calculation formula of periodic energy consumption is put forward. The NHEF model is verified by experiments, and the function λ isconstructed to verify the formula of periodic energy consumption. The calculation results showed that the NHEF model had high accuracy in predicting the dyn

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24

Luo, Albert C. J., and Lidi Chen. "Arbitrary Periodic Motions and Grazing Switching of a Forced Piecewise Linear, Impacting Oscillator." Journal of Vibration and Acoustics 129, no. 3 (2006): 276–84. http://dx.doi.org/10.1115/1.2424971.

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The grazing bifurcation and periodic motion switching of the harmonically forced, piecewise linear system with impacting are investigated. The generic mappings relative to the discontinuous boundaries of this piecewise system are introduced. Based on such mappings, the corresponding grazing conditions are obtained. The mapping structures are developed for the analytical prediction of periodic motions in such a system. The local stability and bifurcation conditions for specified periodic motions are obtained. The regular and grazing, periodic motions are illustrated. The grazing is the origin o

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25

Xin, Haoran, and Tong Cui. "Prediction of Electric Load Neural Network Prediction Model for Big Data." Highlights in Science, Engineering and Technology 104 (June 11, 2024): 155–60. http://dx.doi.org/10.54097/pazjy196.

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In this study, the two-dimensional hexagonal Photonic crystal energy band structure was simulated using COMSOL Multiphysics field simulation software. The 2D hexagonal Photonic crystal structure was constructed by setting parameters such as periodic boundary conditions and air hole radius. Using the frequency domain solver of COMSOL software, the transmission and reflection spectra of the structure were calculated, and the energy band structure diagram was obtained. The effects of different parameters on the energy band structure were analyzed by adjusting the structure parameters. The results

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26

Zou, Yajie, Xuedong Hua, Yanru Zhang, and Yinhai Wang. "Hybrid short-term freeway speed prediction methods based on periodic analysis." Canadian Journal of Civil Engineering 42, no. 8 (2015): 570–82. http://dx.doi.org/10.1139/cjce-2014-0447.

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Short-term traffic speed forecasting is an important issue for developing Intelligent Transportation Systems applications. So far, a number of short-term speed prediction approaches have been developed. Recently, some multivariate approaches have been proposed to consider the spatial and temporal correlation of traffic data. However, as traffic data often demonstrates periodic patterns, the existing methodologies often fail to take into account spatial and temporal information as well as the periodic features of traffic data simultaneously in the multi-step prediction. This paper comprehensive

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27

Lin, Zian, Xiyan Sun, and Yuanfa Ji. "Landslide Displacement Prediction Based on Time Series Analysis and Double-BiLSTM Model." International Journal of Environmental Research and Public Health 19, no. 4 (2022): 2077. http://dx.doi.org/10.3390/ijerph19042077.

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In recent years, machine learning models facilitated notable performance improvement in landslide displacement prediction. However, most existing prediction models which ignore landslide data at each time can provide a different value and meaning. To analyze and predict landslide displacement better, we propose a dynamic landslide displacement prediction model based on time series analysis and a double-bidirectional long short term memory (Double-BiLSTM) model. First, the cumulative landslide displacement is decomposed into trend and periodic displacement components according to time series an

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28

Zhang, Junjia, Zhuorui Li, Enzhi Wang, Bin Yu, Jiangping Li, and Jun Ma. "A Temporal Network Based on Characterizing and Extracting Time Series in Copper Smelting for Predicting Matte Grade." Sensors 24, no. 23 (2024): 7492. http://dx.doi.org/10.3390/s24237492.

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Addressing the issues of low prediction accuracy and poor interpretability in traditional matte grade prediction models, which rely on pre-smelting input and assay data for regression, we incorporate process sensors’ data and propose a temporal network based on Time to Vector (Time2Vec) and temporal convolutional network combined with temporal multi-head attention (TCN-TMHA) to tackle the weak temporal characteristics and uncertain periodic information in the copper smelting process. Firstly, we employed the maximum information coefficient (MIC) criterion to select temporal process sensors’ da

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29

Elder, Benjamin, Matthew Arnold, Anupama Murthi, and Jiří Navrátil. "Learning Prediction Intervals for Model Performance." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 7305–13. http://dx.doi.org/10.1609/aaai.v35i8.16897.

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Understanding model performance on unlabeled data is a fundamental challenge of developing, deploying, and maintaining AI systems. Model performance is typically evaluated using test sets or periodic manual quality assessments, both of which require laborious manual data labeling. Automated performance prediction techniques aim to mitigate this burden, but potential inaccuracy and a lack of trust in their predictions has prevented their widespread adoption. We address this core problem of performance prediction uncertainty with a method to compute prediction intervals for model performance. Ou

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30

Morillon, Benjamin, Charles E. Schroeder, Valentin Wyart, and Luc H. Arnal. "Temporal Prediction in lieu of Periodic Stimulation." Journal of Neuroscience 36, no. 8 (2016): 2342–47. http://dx.doi.org/10.1523/jneurosci.0836-15.2016.

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31

Ichiji, Kei, Noriyasu Homma, Masao Sakai, et al. "A Time-Varying Seasonal Autoregressive Model-Based Prediction of Respiratory Motion for Tumor following Radiotherapy." Computational and Mathematical Methods in Medicine 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/390325.

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To achieve a better therapeutic effect and suppress side effects for lung cancer treatments, latency involved in current radiotherapy devices is aimed to be compensated for improving accuracy of continuous (not gating) irradiation to a respiratory moving tumor. A novel prediction method of lung tumor motion is developed for compensating the latency. An essential core of the method is to extract information valuable for the prediction, that is, the periodic nature inherent in respiratory motion. A seasonal autoregressive model useful to represent periodic motion has been extended to take into a

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32

Huang, Da, Jun He, Yixiang Song, Zizheng Guo, Xiaocheng Huang, and Yingquan Guo. "Displacement Prediction of the Muyubao Landslide Based on a GPS Time-Series Analysis and Temporal Convolutional Network Model." Remote Sensing 14, no. 11 (2022): 2656. http://dx.doi.org/10.3390/rs14112656.

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Landslide displacement prediction is an essential base of landslide hazard prevention, which often needs to establish an accurate prediction model. To achieve accuracy prediction of landslide displacement, a displacement prediction model based on a salp-swarm-algorithm-optimized temporal convolutional network (SSA-TCN) is proposed. The TCN model, consisting of a causal dilation convolution layer residual block, can flexibly increase the receptive fields and capture the global information in a deeper layer. SSA can solve the hyperparameter problem well for TCN model. The Muyubao landslide displ

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33

Chen, Weihang. "Application of Market Cycle Analysis and LSTM in Prediction of Stock Price Movements." BCP Business & Management 38 (March 2, 2023): 856–61. http://dx.doi.org/10.54691/bcpbm.v38i.3787.

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The stock market prediction has been carried out by several ways in data science using deep learning approaches to capture profitable trading opportunities and making the trading plans. However, it is widely believed there are two main issues involved in it, i.e., efficient market hypothesis and low information noise ratio. Therefore, a prediction based model will be affected by noises thus hard to produce a prediction. In this paper, two methods will be presented for forecasting stock future performance. To be specific, LSTM (long-short time memory) and cycle analysis are implemented to predi

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34

Liu, Bao, Jiahuan Xu, Jiangbo Xi, et al. "A Hybrid VMD-BO-GRU Method for Landslide Displacement Prediction in the High-Mountain Canyon Area of China." Remote Sensing 17, no. 11 (2025): 1953. https://doi.org/10.3390/rs17111953.

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Landslides are major geological hazards that pose serious threats to life and property, particularly in the high-mountain canyon regions of Sichuan, Yunnan, and southeastern Tibet. Displacement prediction plays a critical role in disaster prevention and mitigation. In recent years, machine learning methods based on InSAR data have achieved significant breakthroughs in landslide forecasting. However, models relying solely on a single data-driven approach may fail to fully capture the complex physical mechanisms of landslides, affecting both the reliability and interpretability of predictions. T

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Cui, Chunsheng, Guangshu Xia, Chenyu Jia, and Jie Wen. "A Novel Construction Method and Prediction Framework of Periodic Time Series: Application to State of Health Prediction of Lithium-Ion Batteries." Energies 18, no. 6 (2025): 1438. https://doi.org/10.3390/en18061438.

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Due to the time property of natural phenomena and human activities, time series are very common in our lives. The analysis and study of time series can help us to better understand the world, predict the future and make scientific decisions. Focusing on time series prediction, in this paper we propose a method of constructing non-periodic time series into periodic time series and design a framework for time series prediction based on the constructed periodic time series. The proposed construction method and prediction framework for the periodic time series are then applied to predict the state

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36

Kumar, Sandeep. "Predicting Material Properties with Machine Learning on the Periodic Table." International Journal of Applied and Behavioral Sciences 02, no. 01 (2025): 13–21. https://doi.org/10.70388/ijabs250102.

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Machine learning (ML) has emerged as a transformative tool in materials science, enabling the prediction of material properties with unprecedented accuracy and efficiency. By leveraging the periodic table as a structured feature space, researchers can utilize periodic trends and intrinsic elemental properties to train robust predictive models. This paper explores the intersection of machine learning and the periodic table, detailing methodologies, challenges, and applications in material property prediction. Key case studies are presented to illustrate the potential of ML-driven approaches in

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37

Wang, Chenhui, Gaocong Lin, Cuiqiong Zhou, Wei Guo, and Qingjia Meng. "Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition." Land 13, no. 10 (2024): 1724. http://dx.doi.org/10.3390/land13101724.

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Displacement deformation prediction is critical for landslide disaster monitoring, as a good landslide displacement prediction system helps reduce property losses and casualties. Landslides in the Three Gorges Reservoir Area (TGRA) are affected by precipitation and fluctuations in reservoir water level, and displacement deformation shows a step-like curve. Landslide displacement in TGRA is related to its geology and is affected by external factors. Hence, this study proposes a novel landslide displacement prediction model based on variational mode decomposition (VMD) and a Harris Hawk optimize

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38

Phermphoonphiphat, Ekasit, Tomohiko Tomita, Takashi Morita, Masayuki Numao, and Ken-Ichi Fukui. "Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast." Applied Sciences 11, no. 20 (2021): 9728. http://dx.doi.org/10.3390/app11209728.

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Many machine-learning applications and methods are emerging to solve problems associated with spatiotemporal climate forecasting; however, a prediction algorithm that considers only short-range sequential information may not be adequate to deal with periodic patterns such as seasonality. In this paper, we adopt a Periodic Convolutional Recurrent Network (Periodic-CRN) model to employ the periodicity component in our proposals of the periodic representation dictionary (PRD). Phase shifts and non-stationarity of periodicity are the key components in the model to support. Specifically, we propose

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He, Ji-Huan, Qian Yang, Chun-Hui He, and Abdulrahman Ali Alsolami. "PULL-DOWN INSTABILITY OF THE QUADRATIC NONLINEAR OSCILLATORS." Facta Universitatis, Series: Mechanical Engineering 21, no. 2 (2023): 191. http://dx.doi.org/10.22190/fume230114007h.

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A nonlinear vibration system, over a span of convincing periodic motion, might break out abruptly a catastrophic instability, but the lack of a theoretical tool has obscured the prediction of the outbreak. This paper deploys the amplitude-frequency formulation for nonlinear oscillators to reveal the critically important mechanism of the pseudo-periodic motion, and finds the quadratic nonlinear force contributes to the pull-down phenomenon in each cycle of the periodic motion, when the force reaches a threshold value, the pull-down instability occurs. A criterion for prediction of the pull-down

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Doğan, Erdem. "Short-term Traffic Flow Prediction Using Artificial Intelligence with Periodic Clustering and Elected Set." Promet - Traffic&Transportation 32, no. 1 (2020): 65–78. http://dx.doi.org/10.7307/ptt.v32i1.3154.

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Forecasting short-term traffic flow using historical data is a difficult goal to achieve due to the randomness of the event. Due to the lack of a solid approach to short-term traffic prediction, the researchers are still working on novel approaches. This study aims to develop an algorithm that dynamically updates the training set of models in order to make more accurate predictions. For this purpose, an algorithm called Periodic Clustering and Prediction (PCP) has been developed for use in short-term traffic forecasting. In this study, PCP was used to improve Artificial Neural Networks (ANN) p

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Dai, Bingcun, Fan Zhang, Domenico Tarzia, and Kwangwon Ahn. "Forecasting Financial Crashes: Revisit to Log-Periodic Power Law." Complexity 2018 (August 1, 2018): 1–12. http://dx.doi.org/10.1155/2018/4237471.

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We aim to provide an algorithm to predict the distribution of the critical times of financial bubbles employing a log-periodic power law. Our approach consists of a constrained genetic algorithm and an improved price gyration method, which generates an initial population of parameters using historical data for the genetic algorithm. The key enhancements of price gyration algorithm are (i) different window sizes for peak detection and (ii) a distance-based weighting approach for peak selection. Our results show a significant improvement in the prediction of financial crashes. The diagnostic ana

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Lei, Yu, Min Guo, Hongbing Cai, Dandan Hu, and Danning Zhao. "Prediction of Length-of-day Using Gaussian Process Regression." Journal of Navigation 68, no. 3 (2015): 563–75. http://dx.doi.org/10.1017/s0373463314000927.

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The predictions of Length-Of-Day (LOD) are studied by means of Gaussian Process Regression (GPR). The EOP C04 time-series with daily values from the International Earth Rotation and Reference Systems Service (IERS) serve as the data basis. Firstly, well known effects that can be described by functional models, for example effects of the solid Earth and ocean tides or seasonal atmospheric variations, are removed a priori from the C04 time-series. Only the differences between the modelled and actual LOD, i.e. the irregular and quasi-periodic variations, are employed for training and prediction.

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Brée, David S., Damien Challet, and Pier Paolo Peirano. "Prediction accuracy and sloppiness of log-periodic functions." Quantitative Finance 13, no. 2 (2013): 275–80. http://dx.doi.org/10.1080/14697688.2011.607467.

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Sereno, Sergio Gabriele Maria. "Prediction, accommodation and the periodic table: a reappraisal." Foundations of Chemistry 22, no. 3 (2020): 477–88. http://dx.doi.org/10.1007/s10698-020-09371-7.

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Rao, Heng, Yu Gu, Jason Zipeng Zhang, Ge Yu, Yang Cao, and Minghan Chen. "Hierarchical Gradient-Based Genetic Sampling for Accurate Prediction of Biological Oscillations." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 27126–34. https://doi.org/10.1609/aaai.v39i25.34920.

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Biological oscillations are periodic changes in various signaling processes crucial for the proper functioning of living organisms. These oscillations are modeled by ordinary differential equations, with coefficient variations leading to diverse periodic behaviors, typically measured by oscillatory frequencies. This paper explores sampling techniques for neural networks to model the relationship between system coefficients and oscillatory frequency. However, the scarcity of oscillations in the vast coefficient space results in many samples exhibiting non-periodic behaviors, and small coefficie

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Luo, D., and G. Z. Zhang. "A multiperiod grey prediction model and its application." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 11577–86. http://dx.doi.org/10.3233/jifs-202775.

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The purpose of this paper is to solve the prediction problem of nonlinear sequences with multiperiodic features, and a multiperiod grey prediction model based on grey theory and Fourier series is established. For nonlinear sequences with both trend and periodic features, the empirical mode decomposition method is used to decompose the sequences into several periodic terms and a trend term; then, a grey model is used to fit the trend term, and the Fourier series method is used to fit the periodic terms. Finally, the optimization parameters of the model are solved with the objective of obtaining

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EZHOV, D. V., A. N. GAYDADIN, and V. V. KLIMOV. "PREDICTION OF POLYMER COMPATIBILITY FOR POLYMER ELECTROLYTES USING SEMI-EMPIRICAL QUANTUM CHEMICAL METHOD." IZVESTIA VOLGOGRAD STATE TECHNICAL UNIVERSITY, no. 12(295) (December 2024): 109–17. https://doi.org/10.35211/1990-5297-2024-12-295-109-117.

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The article proposes a method for predicting the compatibility of polymers for polymer electrolytes using the semi-empirical PM3 method, as well as the PM3 method using boundary conditions called a periodic box. Using the proposed method, a compatibility prediction was made for polymer pairs: ethylene-propylene rubber and polyethylene, polyvinylidene fluoride and hydrogenated nitrile butadiene rubber.

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Liu, Shipeng, Dejun Ning, and Jue Ma. "TCNformer Model for Photovoltaic Power Prediction." Applied Sciences 13, no. 4 (2023): 2593. http://dx.doi.org/10.3390/app13042593.

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Despite the growing capabilities of the short-term prediction of photovoltaic power, we still face two challenges to longer time-range predictions: error accumulation and long-term time series feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic power, this paper proposes a seq2seq prediction model TCNformer, which outperforms other state-of-the-art (SOTA) algorithms by introducing variable selection (VS), long- and short-term time series feature extraction (LSTFE), and one-step temporal convolutional network (TCN) decoding. A VS module employs corr

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Cao, Yukun, LIsheng Wang, and Luobin Huang. "DPCL-Diff：Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 14 (2025): 14806–14. https://doi.org/10.1609/aaai.v39i14.33623.

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Temporal knowledge graph (TKG) reasoning that infers future missing facts is an essential and challenging task. Predicting future events typically relies on closely related historical facts, yielding more accurate results for repetitive or periodic events. However, for future events with sparse historical interactions, the effectiveness of this method, which focuses on leveraging high-frequency historical information, diminishes. Recently, the capabilities of diffusion models in image generation have opened new opportunities for TKG reasoning. Therefore, we propose a graph node diffusion model

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Wu, Hong, Haipeng Liu, Huaiping Jin, and Yanping He. "Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data." Energies 17, no. 18 (2024): 4739. http://dx.doi.org/10.3390/en17184739.

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Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning and operation of existing power systems. To enhance the accuracy of PV power prediction and ensure the safe operation of the power system, a novel approach based on seasonal division and a periodic attention mechanism (PAM) for PV power prediction is proposed. First, the dataset is divided into three components of trend, period, and residual under fuzzy c-means clustering (FCM) and the seasonal decomposition (SD) method according to four seasons. Three independent bidirectional lon

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