Journal articles: 'Periodic prediction'

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 (May 20, 2021): 4684. http://dx.doi.org/10.3390/app11104684.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 kurtosis criteria were first applied for data decomposition and construction of trend and periodic components. Second, a polynomial regression model and RNN with maximal information coefficient (MIC)-based input variable selection were implemented for individual prediction of trend and periodic components independently. Finally, the predictions of trend and periodic components were aggregated into a final ensemble prediction. The experimental results from the Muyubao landslide demonstrate that the proposed EEMD-RNN decomposition-ensemble learning model is capable of increasing prediction accuracy and outperforms the traditional decomposition-ensemble learning models (including EEMD-support vector machine, and EEMD-extreme learning machine). Moreover, compared with standard RNN, the gated recurrent unit (GRU)-and long short-term memory (LSTM)-based models perform better in predicting accuracy. The EEMD-RNN decomposition-ensemble learning model is promising for landslide displacement prediction.

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 short-term speed prediction under different scenarios: (1) multi-horizon ahead prediction (5, 15, 30, 60 minutes ahead predictions), (2) with and without periodic component, (3) two data aggregation levels (5-minute and 15-minute), (4) peak hours and off-peak hours. Specifically, three statistical models (i.e., space time (ST) model, vector autoregressive (VAR) model, autoregressive integrated moving average (ARIMA) model) and three machine learning approaches (i.e., support vector machines (SVM) model, multi-layer perceptron (MLP) model, recurrent neural network (RNN) model) are developed and examined. Furthermore, the periodic features of the speed data are considered via a hybrid prediction method, which assumes that the data consist of two components: a periodic component and a residual component. The periodic component is described by a trigonometric regression function, and the residual component is modeled by the statistical models or the machine learning approaches. The important conclusions can be summarized as follows: (1) the multi-step ahead prediction accuracy improves when considering the periodic component of speed data for both three statistical models and three machine learning models, especially in the peak hours; (2) considering the impact of periodic component for all models, the prediction performance improvement gradually becomes larger as the time step increases; (3) under the same prediction horizon, the prediction performance of all models for 15-minute speed data is generally better than that for 5-minute speed data. Overall, the findings in this paper suggest that the proposed hybrid prediction approach is effective for both statistical and machine learning models in short-term speed prediction.

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 model). In order to produce more accurate predictions, background-value optimization is specially carried out using the particle swarm optimization technique in conventional GM (1, 1). For verifying PGMSW, it is utilized in the prediction of the cyclic fluctuation of telemetry series data and exponential variations therein. The simulation results indicate that the PGMSW model provides accurate solutions for prediction problems similar to the pseudo-periodic telemetry series.

4

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 (January 2017): 52–66. http://dx.doi.org/10.4018/ijalr.2017010104.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 property. In general, future prediction using machine learning is based on the tendency obtained through past training or learning. In this case, an optimal action will be determined uniquely based on a property of disturbance. However, in this type of situation, the learning time increases in proportional to the amount of training data, either, the tendency may not be found using prediction, in the worst case. In this paper, we focus on prediction for almost-periodic disturbance. In particular, we consider the situation where almost-periodic disturbance signals occur. From this perspective, we propose a method that identifies the frequency of an almost- periodic function based on the frequency of the disturbance using Fourier transform, nearest-neighbor one-step-ahead forecasts and Nyquist-Shannon sampling theorem.

5

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 (May 25, 2023): e0286325. http://dx.doi.org/10.1371/journal.pone.0286325.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 used to adjust the proportion of the predicted value of the fuzzy time series and the periodic observation value. A fund revenue prediction model and a fund expenditure prediction model are constructed, and an experiment is carried out with the actual financial data of a university in China. The experimental result shows the effectiveness of the proposed model, which can provide solid references for financial management and funds risk prevention in universities.

6

Cheng, Weiwei, Guigen Nie, and Jian Zhu. "Characterizing Periodic Variations of Atomic Frequency Standards via Their Frequency Stability Estimates." Sensors 23, no. 11 (June 5, 2023): 5356. http://dx.doi.org/10.3390/s23115356.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 are independent of the variances of the stochastic component. The proposed model is tested against simulated and real clock data, revealing that our approach provides more precise characterization of periodic variations compared to the least squares method. Additionally, we observe that overfitting periodic variations can improve the precision of GPS clock bias prediction, as indicated by a comparison of fitting and prediction errors of satellite clock bias.

7

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Pawelzik, K., and H. G. Schuster. "Unstable periodic orbits and prediction." Physical Review A 43, no. 4 (February 1, 1991): 1808–12. http://dx.doi.org/10.1103/physreva.43.1808.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 compared the impacts of three periodic functions on multistep ahead travel time prediction for different temporal scales (5-minute, 10-minute, and 15-minute). The results indicate that the periodic functions can improve the prediction performance of machine learning models for more than 60 minutes ahead prediction and improve the over 30 minutes ahead prediction accuracy for statistical models. Three periodic functions show a slight difference in improving the prediction accuracy of the six prediction models. For the same prediction step, the effect of the periodic function is more obvious at a higher level of aggregation.

10

Zhao, Lin, Nan Li, Hui Li, Renlong Wang, and Menghao Li. "BDS Satellite Clock Prediction Considering Periodic Variations." Remote Sensing 13, no. 20 (October 11, 2021): 4058. http://dx.doi.org/10.3390/rs13204058.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 the prediction accuracy of the SAM. In this paper, we investigate the periodic noise and its variations present in BDS clock offsets, and improve the clock prediction model by considering the periodic variations. The periodic noise and its variations over time are analyzed and quantified by short time Fourier transform (STFT). The results show that both the amplitude and frequency of the main periodic term in BDS clock offsets vary with time. To minimize the impact of periodic variations on clock prediction, a time frequency analysis model (TFAM) based on STFT is constructed, in which the periodic term can be quantified and compensated accurately. The experiment results show that both the fitting and prediction accuracy of TFAM are better than SAM. Compared with SAM, the average improvement of the prediction accuracy using TFAM of the 6 h, 12 h, 18 h and 24 h is in the range of 6.4% to 10% for the GNSS Research Center of Wuhan University (WHU) clock offsets, and 11.1% to 14.4% for the Geo Forschungs Zentrum (GFZ) clock offsets. For the satellites C06, C14, and C32 with marked periodic variations, the prediction accuracy is improved by 26.7%, 16.2%, and 16.3% for WHU clock offsets, and 29.8%, 16.0%, 21.0%, and 9.0% of C06, C14, C28, and C32 for GFZ clock offsets.

11

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Doğan, Erdem. "Short-term Traffic Flow Prediction Using Artificial Intelligence with Periodic Clustering and Elected Set." Promet - Traffic&Transportation 32, no. 1 (February 6, 2020): 65–78. http://dx.doi.org/10.7307/ptt.v32i1.3154.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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) predictive performance by improving the training set of ANN to predict short-term traffic flow using selected clusters. A large amount of traffic data collected from the US and UK motorways was used to determine the PCP ability to increase the ANN performance. The robustness of the proposed approach was determined by the performance measures used in the literature and the mean prediction errors of PCP were significantly below other approaches. In addition, the studies showed that the percentage errors of PCP predictions decreased in response to increasing traffic flow values. Considering the obtained positive results, this method can be used in real-time traffic control systems and in different areas needed.

13

Gong, Lixiong, and Canlin Wang. "Model of Automobile Parts Sale Prediction Based on Nonlinear Periodic Gray GM(1,1) and Empirical Research." Mathematical Problems in Engineering 2019 (August 27, 2019): 1–8. http://dx.doi.org/10.1155/2019/3620120.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

The traditional predictive method cannot fully reflect the complex nonlinear characteristics and regularities of automobile and parts sales data, so the prediction precision is not high. The purpose of this paper is to propose the gray GM(1,1) nonlinear periodic predictive model by introducing the seasonal variation index to improve predictive accuracy of the single GM(1,1) model. Firstly, the paper analyzes concept of GM(1,1) and then proposes the gray GM(1,1) nonlinear periodic predictive model to forecast automobile parts sales. The model algorithm used gray theory and accumulated technology to generate new data and set up unified differential equations to find the fitting curve of automobile parts sales prediction by the seasonal variation index to remove random elements. Lastly, the gray GM(1,1) nonlinear periodic predictive model is used for empirical analysis; the result of example shows that the model proposed in the paper is feasible. The superiority of the proposed predictive model compared with the single gray GM(1,1) model is demonstrated. The reliability of this model is experienced by the accuracy test, which provides a theoretical guidance for the prediction of automobile part sales. And the average relative error is reduced by 8.52% compared with the single GM(1,1) model.

14

Zhang, Junrong, Huiming Tang, Dwayne D. Tannant, Chengyuan Lin, Ding Xia, Yankun Wang, and Qianyun Wang. "A Novel Model for Landslide Displacement Prediction Based on EDR Selection and Multi-Swarm Intelligence Optimization Algorithm." Sensors 21, no. 24 (December 14, 2021): 8352. http://dx.doi.org/10.3390/s21248352.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 separate the trend and periodic displacements from the observed cumulative landslide displacement. The frequency component and residual component of reconstructed inducing factors that related to landslide movements are also extracted by the CEEMD and t-test, and then picked out with edit distance on real sequence (EDR) as input variables for the support vector regression (SVR) model. MSI optimization algorithms are used to optimize the SVR model in the MSI optimization; thus, six predictions models can be obtained that can be used in the displacement prediction part. Finally, the trend and periodic displacements are predicted by six optimized SVR models, respectively. The trend displacement and periodic displacement with the highest prediction accuracy are added and regarded as the final prediction result. The case study of the Shiliushubao landslide shows that the prediction results match the observed data well with an improvement in the aspect of average relative error, which indicates that the proposed model can predict landslide displacements with high precision, even when the displacements are characterized by stepped curves that under the influence of multiple time-varying factors.

15

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 (March 1, 2022): 012063. http://dx.doi.org/10.1088/1742-6596/2216/1/012063.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 windows. Finally, one-shot and feedback models of the LSTM algorithm were employed to predict network anomalies. The experiment showed that the best prediction accuracy for this method is over 80% with both one-shot and feedback models, when the prediction time window is 12h.

16

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 (April 30, 2024): 537. http://dx.doi.org/10.3390/sym16050537.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 spatial feature extraction methods, we introduce a maximum mutual information coefficient spatial feature extraction method. In terms of periodic feature extraction methods, we introduce a periodic trend modeling method into the prediction of speed time series, and effective fusion is carried out. Four strategies are evaluated based on the Guangzhou road speed dataset: a univariate polynomial model, a spatiotemporal polynomial model, a periodic polynomial model, and a spatiotemporal periodic polynomial model. The test results show that the three methods proposed in this article can effectively improve prediction accuracy. Comparing the spatiotemporal periodic polynomial model with multiple machine learning models and deep learning models, the prediction accuracy is improved by 5.94% compared to the best feedforward neural network. The research in this article can effectively deal with the temporal, spatial, periodic, and nonlinear characteristics of speed prediction, and to a certain extent, improve the accuracy of speed prediction.

17

Chen, Guisheng, and Zhanshan Li. "A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation." Entropy 23, no. 11 (October 29, 2021): 1430. http://dx.doi.org/10.3390/e23111430.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers’ individual shopping records, where tendency is a new concept to reflect the evolution of customers’ shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.

18

Chen, Long, Minghua Lin, and Liankun Chen. "Financial Risk Prediction based on Time Series." Academic Journal of Management and Social Sciences 5, no. 1 (November 5, 2023): 169–74. http://dx.doi.org/10.54097/ajmss.v5i1.14073.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

In this paper, the data mining method based on time series is applied to financial risk prediction, the corresponding model is established, and the predictive ability of the model is tested according to the real financial data of K company. The actual test results show that the time series method is suitable for the prediction of financial data with regular periodic changes, and it has high accuracy.

19

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 considered to some extent via multi-scale periodic pattern explored basing on historical energy data. This work is an exploration for medium and long-term wind power forecasting that can well adapt to existing conditions. It has better prediction accuracy than the method without multi-scale periodicity considered.

20

Morillon, Benjamin, Charles E. Schroeder, Valentin Wyart, and Luc H. Arnal. "Temporal Prediction in lieu of Periodic Stimulation." Journal of Neuroscience 36, no. 8 (February 24, 2016): 2342–47. http://dx.doi.org/10.1523/jneurosci.0836-15.2016.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

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 (May 18, 2021): 7305–13. http://dx.doi.org/10.1609/aaai.v35i8.16897.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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. Our methodology uses transfer learning to train an uncertainty model to estimate the uncertainty of model performance predictions. We evaluate our approach across a wide range of drift conditions and show substantial improvement over competitive baselines. We believe this result makes prediction intervals, and performance prediction in general, significantly more practical for real-world use.

22

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 (February 24, 2021): 12–21. http://dx.doi.org/10.5755/j02.mech.25630.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 dynamic and quasi-static NHEF of rubber spring, the prediction accuracy of shear condition was better than that of compression condition, and the accuracy of quasi-static condition was better than that of dynamic condition; the calculation formula of periodic energy consumption had a good prediction accuracy in all working conditions.

23

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 (November 1, 2006): 276–84. http://dx.doi.org/10.1115/1.2424971.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 of the periodic motion switching for this system. Such a grazing bifurcation cannot be estimated through the local stability analysis. This model is applicable to prediction of periodic motions in nonlinear dynamics of gear transmission systems.

24

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 (August 2015): 570–82. http://dx.doi.org/10.1139/cjce-2014-0447.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 comprehensively evaluated the multi-step prediction performance of space time (ST) model, vector autoregression (VAR), and autoregressive integrated moving average (ARIMA) models using the 5 minute freeway speed data collected from five loop detectors located on an eastbound segment of Interstate 394 freeway, in Minnesota. To further consider the cyclical characteristics of freeway speed data, hybrid prediction approaches were proposed to decompose speed into two different components: a periodic trend and a residual part. A trigonometric regression function is introduced to capture the periodic component and the residual part is modeled by the ST, VAR, and ARIMA models. The prediction results suggest that for multi-step freeway speed prediction, as the time step increases, the ST model demonstrates advantages over the VAR and ARIMA models. Comparisons among the ST, VAR, ARIMA, and hybrid models demonstrated that modeling the periodicity and the residual part separately can better interpret the underlining structure of the speed data. The proposed hybrid prediction approach can accommodate the periodic trends and provide more accurate prediction results when the forecasting horizon is greater than 30 min.

25

Hwang, Eunju. "Improvement on Forecasting of Propagation of the COVID-19 Pandemic through Combining Oscillations in ARIMA Models." Forecasting 6, no. 1 (December 26, 2023): 18–35. http://dx.doi.org/10.3390/forecast6010002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Daily data on COVID-19 infections and deaths tend to possess weekly oscillations. The purpose of this work is to forecast COVID-19 data with partially cyclical fluctuations. A partially periodic oscillating ARIMA model is suggested to enhance the predictive performance. The model, optimized for improved prediction, characterizes and forecasts COVID-19 time series data marked by weekly oscillations. Parameter estimation and out-of-sample forecasting are carried out with data on daily COVID-19 infections and deaths between January 2021 and October 2022 in the USA, Germany, and Brazil, in which the COVID-19 data exhibit the strongest weekly cycle behaviors. Prediction accuracy measures, such as RMSE, MAE, and HMAE, are evaluated, and 95% prediction intervals are constructed. It was found that predictions of daily COVID-19 data can be improved considerably: a maximum of 55–65% in RMSE, 58–70% in MAE, and 46–60% in HMAE, compared to the existing models. This study provides a useful predictive model for the COVID-19 pandemic, and can help institutions manage their healthcare systems with more accurate statistical information.

26

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 (February 12, 2022): 2077. http://dx.doi.org/10.3390/ijerph19042077.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 analysis via the exponentially weighted moving average (EWMA) method. We consider that trend displacement is mainly influenced by landslide factors, and we apply a BiLSTM model to predict landslide trend displacement. This paper analyzes the internal relationship between rainfall, reservoir level and landslide periodic displacement. We adopt the maximum information coefficient (MIC) method to calculate the correlation between influencing factors and periodic displacement. We employ the BiLSTM model for periodic displacement prediction. Finally, the model is validated against data pertaining to the Baishuihe landslide in the Three Gorges, China. The experimental results and evaluation indicators demonstrate that this method achieves a better prediction performance than the classical prediction methods, and landslide displacement can be effectively predicted.

27

Ichiji, Kei, Noriyasu Homma, Masao Sakai, Yuichiro Narita, Yoshihiro Takai, Xiaoyong Zhang, Makoto Abe, Norihiro Sugita, and Makoto Yoshizawa. "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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 account the fluctuation of periodic nature in respiratory motion. The extended model estimates the fluctuation by using a correlation-based analysis for adaptation. The prediction performance of the proposed method was evaluated by using data sets of actual tumor motion and compared with those of the state-of-the-art methods. The proposed method demonstrated a high performance within submillimeter accuracy. That is, the average error of 1.0 s ahead predictions was0.931±0.055 mm. The accuracy achieved by the proposed method was the best among those by the others. The results suggest that the method can compensate the latency with sufficient accuracy for clinical use and contribute to improve the irradiation accuracy to the moving tumor.

28

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 (June 1, 2022): 2656. http://dx.doi.org/10.3390/rs14112656.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 displacement collected from a professional GPS monitoring system implemented in 2006 is used to analyze the displacement features of the slope and evaluate the performance of the SSA-TCN model. The cumulative displacement time series is decomposed into trend displacement (linear part) and periodic displacement (nonlinear part) by the variational modal decomposition (VMD) method. Then, a polynomial function is used to predict the trend displacement, and the SSA-TCN model is used to predict the periodic displacement of the landslide based on considering the response relationship between periodic displacement, rainfall, and reservoir water. This research also compares the proposed approach results with the other popular machine learning and deep learning models. The results demonstrate that the proposed hybrid model is superior to and more effective and accurate than the others at predicting the landslide displacement.

29

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 predict the future period that gives a higher return than average times. According to the analysis, introducing the time analysis as a variable to input could significantly increase the accuracy of predicting the return for the next few weeks. These results shed light on guiding further exploration of the different ways of extracting periodic behaviors of the market and marking predictions based on the analysis.

30

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

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Sereno, Sergio Gabriele Maria. "Prediction, accommodation and the periodic table: a reappraisal." Foundations of Chemistry 22, no. 3 (June 6, 2020): 477–88. http://dx.doi.org/10.1007/s10698-020-09371-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

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 (January 19, 2015): 563–75. http://dx.doi.org/10.1017/s0373463314000927.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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. Different input patterns are discussed and compared so as to optimise the GPR model. The optimal patterns have been found in terms of the prediction accuracy and efficiency, which conduct the multi-step ahead predictions utilising the formerly predicted values as inputs. Finally, the results of the predictions are analysed and compared with those obtained by other prediction methods. It is shown that the accuracy of the predictions are comparable with that of other prediction methods. The developed method is easy to use.

33

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 analysis further demonstrates the accuracy, efficiency, and stability of our predictions.

34

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 (October 18, 2021): 9728. http://dx.doi.org/10.3390/app11209728.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 a Soft Periodic-CRN (SP-CRN) with three proposals of utilizing periodicity components: nearby-time (PRD-1), periodic-depth (PRD-2), and periodic-depth differencing (PRD-3) representation to improve climate forecasting accuracy. We experimented on geopotential height at 300 hPa (ZH300) and sea surface temperature (SST) datasets of ERA-Interim. The results showed the superiority of PRD-1 plus or minus one month of a prior cycle to capture the phase shift. In addition, PRD-3 considered only the depth of one differencing periodic cycle (i.e., the previous year) can significantly improve the prediction accuracy of ZH300 and SST. The mixed method of PRD-1, and PRD-3 (SP-CRN-1+3) showed a competitive or slight improvement over their base models. By adding the metadata component to indicate the month with one-hot encoding to SP-CRN-1+3, the prediction result was a drastic improvement. The results showed that the proposed method could learn four years of periodicity from the data, which may relate to the El Niño–Southern Oscillation (ENSO) cycle.

35

Liu, Shipeng, Dejun Ning, and Jue Ma. "TCNformer Model for Photovoltaic Power Prediction." Applied Sciences 13, no. 4 (February 17, 2023): 2593. http://dx.doi.org/10.3390/app13042593.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 correlation analysis and periodic analysis to separate the time series correlation information, LSTFE extracts multiple time series features from time series data, and one-step TCN decoding realizes generative predictions. We demonstrate here that TCNformer has the lowest mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in contrast to the other algorithms in the field of the short-term prediction of photovoltaic power, and furthermore, the effectiveness of each module has been verified through ablation experiments.

36

Luo, D., and G. Z. Zhang. "A multiperiod grey prediction model and its application." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11577–86. http://dx.doi.org/10.3233/jifs-202775.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 a minimum mean square error. The novel model is applied to research on the loss rate of agricultural droughts in Henan Province. The average absolute error and root mean square error of the empirical analysis are 0.3960 and 0.5086, respectively. The predicted results show that the novel model can effectively fit the loss rate sequence. Compared with other models, the novel model has higher prediction accuracy and is suitable for the prediction of multiperiod sequences.

37

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 (August 10, 2023): 191. http://dx.doi.org/10.22190/fume230114007h.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

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 instability is proposed and verified numerically.

38

Huang, Bingqing, Haonan Zheng, Xinbo Guo, Yi Yang, and Ximing Liu. "A Novel Model Based on DA-RNN Network and Skip Gated Recurrent Neural Network for Periodic Time Series Forecasting." Sustainability 14, no. 1 (December 29, 2021): 326. http://dx.doi.org/10.3390/su14010326.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Deep learning models are playing an increasingly important role in time series forecasting with their excellent predictive ability and the convenience of not requiring complex feature engineering. However, the existing deep learning models still have shortcomings in dealing with periodic and long-distance dependent sequences, which lead to unsatisfactory forecasting performance on this type of dataset. To handle these two issues better, this paper proposes a novel periodic time series forecasting model based on DA-RNN, called DA-SKIP. Using the idea of task decomposition, the novel model, based on DA-RNN, GRU-SKIP and autoregressive component, breaks down the prediction of periodic time series into three parts: linear forecasting, nonlinear forecasting and periodic forecasting. The results of the experiments on Solar Energy, Electricity Consumption and Air Quality datasets show that the proposed model outperforms the three comparison models in capturing periodicity and long-distance dependence features of sequences.

39

Zhao, D. W., G. H. Su, Z. H. Liang, Y. J. Zhang, and S. Z. Qiu. "ICONE19-43196 Prediction of Periodic Dryout Critical Heat Flux under Flow Oscillation Condition." Proceedings of the International Conference on Nuclear Engineering (ICONE) 2011.19 (2011): _ICONE1943. http://dx.doi.org/10.1299/jsmeicone.2011.19._icone1943_75.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

LUO, ALBERT C. J., and BING XUE. "AN ANALYTICAL PREDICTION OF PERIODIC FLOWS IN THE CHUA CIRCUIT SYSTEM." International Journal of Bifurcation and Chaos 19, no. 07 (July 2009): 2165–80. http://dx.doi.org/10.1142/s0218127409023998.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

In this paper, periodic and chaotic behaviors in the Chua circuit system are investigated, and the analytical prediction of periodic flows in such a system is carried out. The solutions of the system in different regions with different parameters are first obtained. The switching boundaries are introduced for systems switching because of different system parameters in different domains. In the vicinity of the switching boundaries, the normal vector-field product is introduced to measure flow switching on the separation boundary, and the conditions for grazing and passable flows to the discontinuous boundary are presented. The basic mappings are defined and periodic responses of such a system are predicted analytically from mapping structures. The local stability and bifurcation analysis are carried out.

41

OI, Akira, Bumpei MIYAZAKI, Kengo OBAMA, Kiyoyuki KAITO, Kiyoshi KOBAYASHI, and Kiyohito YAMAGUCHI. "DETERIORATION PREDICTION OF EXPRESSWAY PAVEMENT WITH PERIODIC FWD DATA." Journal of Japan Society of Civil Engineers, Ser. E1 (Pavement Engineering) 70, no. 2 (2014): 11–25. http://dx.doi.org/10.2208/jscejpe.70.11.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Kim, Hyung-Il. "Outlier prediction in sensor network data using periodic pattern." Journal of Sensor Science and Technology 15, no. 6 (November 30, 2006): 433–41. http://dx.doi.org/10.5369/jsst.2006.15.6.433.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Kang, Bokyoung, Dongsoo Kim, and Suk‐Ho Kang. "Periodic performance prediction for real‐time business process monitoring." Industrial Management & Data Systems 112, no. 1 (January 27, 2012): 4–23. http://dx.doi.org/10.1108/02635571211193617.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Schwartz, Greg, Rob Harris, David Shrom, and Michael J. Berry. "Detection and prediction of periodic patterns by the retina." Nature Neuroscience 10, no. 5 (April 22, 2007): 552–54. http://dx.doi.org/10.1038/nn1887.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Lund, Robert, and I. V. Basawa. "Recursive Prediction and Likelihood Evaluation for Periodic ARMA Models." Journal of Time Series Analysis 21, no. 1 (January 2000): 75–93. http://dx.doi.org/10.1111/1467-9892.00174.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Worrall, John. "Prediction and the ‘periodic law’: a rejoinder to Barnes." Studies in History and Philosophy of Science Part A 36, no. 4 (December 2005): 817–26. http://dx.doi.org/10.1016/j.shpsa.2005.08.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Baier, G., T. M. Grateful, M. D. Graham, and E. N. Lightfoot. "Prediction of mass transfer rates in spatially periodic flows." Chemical Engineering Science 54, no. 3 (February 1999): 343–55. http://dx.doi.org/10.1016/s0009-2509(98)00234-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Bittanti, S., and G. De Nicolao. "Periodic ARMA Models: Optimal Prediction and Minimum-Phase Condition." IFAC Proceedings Volumes 23, no. 8 (August 1990): 449–54. http://dx.doi.org/10.1016/s1474-6670(17)51956-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Zhang, Kaimeng, Chi Tim Ng, and Myung Hwan Na. "Real time prediction of irregular periodic time series data." Journal of Forecasting 39, no. 3 (January 6, 2020): 501–11. http://dx.doi.org/10.1002/for.2637.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Liu, Zhigang, Jin Wang, Tao Tao, Ziyun Zhang, Siyi Chen, Yang Yi, Shuang Han, and Yongqian Liu. "Wave Power Prediction Based on Seasonal and Trend Decomposition Using Locally Weighted Scatterplot Smoothing and Dual-Channel Seq2Seq Model." Energies 16, no. 22 (November 9, 2023): 7515. http://dx.doi.org/10.3390/en16227515.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Abstract:

Wave energy has emerged as a focal point in marine renewable energy research. Accurate prediction of wave power plays a pivotal role in enhancing power supply reliability. This paper introduces an innovative wave power prediction method that combines seasonal–trend decomposition using LOESS (STL) with a dual-channel Seq2Seq model. The decomposition model addresses the issue of component redundancy in current input decomposition methods, thereby uncovering key components. The prediction model improves upon the limitations of current prediction models that directly concatenate multiple features, allowing for a more detailed consideration of both trend and periodic features. The proposed approach begins by decomposing the power sequence based on tidal periods and optimal correlation criteria, effectively extracting both trend and periodic features. Subsequently, a dual-channel Seq2Seq model is constructed. The first channel employs temporal pattern attention to capture the trend and stochastic fluctuation information, while the second channel utilizes multi-head self-attention to further enhance the extraction of periodic components. Model validation is performed using data from two ocean buoys, each with a five-year dataset. The proposed model achieves an average 2.45% reduction in RMSE compared to the state-of-the-art method. Both the decomposition and prediction components of the model contribute to this increase in accuracy.

Journal articles on the topic 'Periodic prediction'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles