Dynamic Time Series Prediction Research Articles

Real-time ocean wave prediction in time domain with autoregression and echo state networks

This study evaluates the potential of applying echo state networks (ESN) and autoregression (AR) for dynamic time series prediction of free surface elevation for use in wave energy converters (WECs). The performance of these models is evaluated on time series data at different water depths and wave conditions, including both measured and simulated data with a focus on real-time prediction of ocean waves at a given location without resolving for the surrounding ocean surface, in other words, short-time single-point forecasting. The work presented includes training the models on historical wave data and testing their ability to predict phase-resolved future surface wave patterns for short-time forecasts. Additionally, this study discusses the feasibility of deploying these models for extended time intervals. It provides valuable insights into the trade-offs between accuracy and practicality in the real-time implementation of predictive models for wave elevation, which are needed in wave energy converters to optimise the control algorithm.

长白山阔叶红松林紫椴种群结构与动态特征

PDF HTML阅读 XML下载 导出引用 引用提醒 长白山阔叶红松林紫椴种群结构与动态特征 DOI: 10.5846/stxb202106191629 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家科技基础资源调查项目(2019FY101602) Population structure and dynamic characteristics of Tilia amurensis in broad-leaved Korean pine mixed forest in Changbai Mountain Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:紫椴(Tilia amurensis)为国家二级重点保护野生植物。以长白山北坡阔叶红松林内的紫椴种群为研究对象,通过建立种群静态生命表、绘制存活曲线、计算数量动态指数并引入生存函数和时间序列预测模型定量描绘其结构与动态特征。结果表明:1)种群年龄结构呈不规则金字塔形,幼龄阶段(Ⅰ-Ⅳ龄级)个体数之和占个体总数的67.26%。2)种群存活曲线接近Deevey-Ⅲ型,表明植株在幼龄阶段死亡率高。3)总体数量动态变化指数Vpi>V'pi>0,表明紫椴种群为增长型,但V'pi趋近于0,结合死亡率(qx)和消失率(Kx)曲线呈现出连续性先增后降的复杂动态变化趋势,可知该种群不稳定且在干扰情况下增长趋势不明显。4)生存函数分析显示,种群在Ⅲ-Ⅳ龄级间达到平衡状态,尔后进入衰退期且从第Ⅷ龄级开始进入生理衰退。5)时间序列预测分析表明,在未来2、3、4、5、6、7、8、9、10龄级时间后,紫椴幼龄个体数逐渐减小,而中、成和老龄个体数量将逐渐增加,说明未来紫椴种群的增长主要依靠后者。生物学特性和生境是限制紫椴种群增长的主要原因。建议加强幼苗抚育工作,提高幼苗和小树的存活率,保护和改善生存环境,从而促进紫椴种群的自然更新和恢复。 Abstract:Tilia amurensis is the second-class national protection species. In this study, the population structure and dynamic characteristics of T. amurensis distributed in Changbai Mountain were analyzed by population statistics, including static life table, survival curve, the quantity dynamic index, survival function, and time series prediction model. The results showed that:1) the age-class structure of T. amurensis population was irregular pyramid type, and the sum of number of young individuals (I-IV age class) accounted for 67.26% of the total individuals. 2) The survival cure was close to Deevey-Ⅲ type, which showed that the number of young individuals was abound while their survival rate was low. 3) The population dynamics index was Vpi>V'pi>0, indicating that T. amurensis population belonged to the increasing type. However, the index V'pi closed to 0, and the curve of mortality rate (qx) and vanish rate (Kx) showed a complex dynamic trend of continuous increase first and then decrease, which indicated that the population stability was poor and the growth trend was not obvious in the case of disturbance. 4) The analysis of survival function showed that the population reached a balance between the age classes of Ⅲ-Ⅳ, and then entered the decline period. From the age class of Ⅷ, the population entered a physiological decline phase. 5) Time series prediction analysis showed that in the next 2, 3, 4, 5, 6, 7, 8, 9, 10 age classes, the number of young individuals would gradually decrease, while the number of middle-aged, adult and old individuals would gradually increase, indicating that the maintenance of T. amurensis population mainly depends on the latter in the future. The biological characteristics and habitat are the main factors limiting the population growth of T. amurensis. We suggest that the seedling raising should be strengthened to improve the survival rate of the seedlings and small trees, and the habitat should also be protected to enhance natural regeneration and restoration of the T. amurensis population. 参考文献 相似文献 引证文献

Computer dynamic model and time series prediction of air by LSTM recurrent neural network

Air quality is closely related to people's daily life. In order to predict air quality with high accuracy, the air pollution monitoring data of Lanzhou City from May 13, 2015 to April 18, 2020 is used as the basis, and the LSTM model based on the deep learning library TensorFlow is used to predict the air quality of Lanzhou City and compared with the RNN model. The experimental results show that the mean square error of the model is 39.579212, which is more accurate than the RNN model but takes a longer time, and provides a new prediction method with the scientific and reasonable theoretical basis for air pollution prevention and control work.

Co-evolutionary multi-task learning for dynamic time series prediction

Time series prediction typically consists of a data reconstruction phase where the time series is broken into overlapping windows known as the timespan. The size of the timespan can be seen as a way of determining the extent of past information required for an effective prediction. In certain applications such as the prediction of wind-intensity of storms and cyclones, prediction models need to be dynamic in accommodating different values of the timespan. These applications require robust prediction as soon as the event takes place. We identify a new category of problem called dynamic time series prediction that requires a model to give prediction when presented with varying lengths of the timespan. In this paper, we propose a co-evolutionary multi-task learning method that provides a synergy between multi-task learning and co-evolutionary algorithms to address dynamic time series prediction. The method features effective use of building blocks of knowledge inspired by dynamic programming and multi-task learning. It enables neural networks to retain modularity during training for making a decision in situations even when certain inputs are missing. The effectiveness of the method is demonstrated using one-step-ahead chaotic time series and tropical cyclone wind-intensity prediction.

Integrated Proactive Control Model for Energy Efficiency Processes in Facilities Management: Applying Dynamic Exponential Smoothing Optimization

Sustainable facilities management (SFM) opens the door of opportunity for companies to evaluate the quality of resources and environment management at their facilities. It enables the principles of sustainable development. There is still inefficiency in quantitative research of integrating environmental factors, particularly multi-source data, to monitor and control complicated systems in buildings. The objective of this research is to develop an effective method to dynamically optimize energy efficiency in SFM plans and strategies. The research question is: can the integrated proactive method reduce energy consumption with dynamically adjustable controls? This paper proposes a coordinated proactive control method using dynamic time-series prediction (PCM-DTSP) for SFM, which optimizes system controls by integrating the prediction results and monitored environmental-data. The results show that, after optimization, the temperature fluctuations are reduced to 33.3%. The average temperature and maximum temperature are reduced by 8% and 13.1%, respectively. The instantaneous power consumption was reduced by 0.17 KW per hour for each cooling system unit. The PCM-DTSP method can significantly optimize energy efficiency, which paves the way for long-term comprehensive energy management. The contribution of the research lies in its optimized control of energy consumption, temperature stabilization, and improvement of environmental comfort solutions, which can be generalized to various types of buildings.

Co-evolutionary multi-task learning with predictive recurrence for multi-step chaotic time series prediction

Multi-task learning employs a shared representation of knowledge for learning several instances of the same problem. Multi-step time series problem is one of the most challenging problems for machine learning methods. The performance of a prediction model face challenges for higher prediction horizons due to the accumulation of errors. Cooperative coevolution employs in a divide and conquer approach for training neural networks and has been very promising for single step ahead time series prediction. Recently, co-evolutionary multi-task learning has been proposed for dynamic time series prediction. In this paper, we adapt co-evolutionary multi-task learning for multi-step prediction where predictive recurrence is developed to feature knowledge from previous states for future prediction horizon. The goal of the paper is to present a network architecture with predictive recurrence which is capable of multi-step prediction through a form of multi-task learning. We employ cooperative neuro-evolution and an evolutionary algorithm as baselines for comparison. The results show that the proposed method provides the best generalization performance in most cases. Comparison of results with the literature has shown to be promising which motivates further application of the approach for related real-world problems.

Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions

Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous studies have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors’ influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions.

Distance Prediction for Commercial Serial Crime Cases Using Time Delay Neural Networks

The prediction of the next serial criminal time is important in the field of criminology for preventing the recurring actions of serial criminals. In the associated dynamic systems, one of the main sources of instability and poor performances is the time delay, which is commonly predicted based on nonlinear methods. The aim of this study is to introduce a dynamic neural network model by using nonlinear autoregressive time series with exogenous (external) input (NARX) and Back Propagation Through Time (BPTT), which is verified intensively with MATLAB to predict and model the crime times for the next distance of serial cases. Recurrent neural networks have been extensively used for modeling of nonlinear dynamic systems. There are different types of recurrent neural networks such as Time Delay Neural Networks (TDNN), layer recurrent networks, NARX, and BPTT. The NARX model for the two cases of input- output modeling of dynamic systems and time series prediction draw more attention. In this study, a comparison of two models of NARX and BPTT used for the prediction of the next serial criminal time illustrates that the NARX model exhibits better performance for the prediction of serial cases than the BPTT model. Our future work aims to improve the NARX model by combining objective functions.

Learning grammatical structure with Echo State Networks

Echo State Networks (ESNs) have been shown to be effective for a number of tasks, including motor control, dynamic time series prediction, and memorizing musical sequences. However, their performance on natural language tasks has been largely unexplored until now. Simple Recurrent Networks (SRNs) have a long history in language modeling and show a striking similarity in architecture to ESNs. A comparison of SRNs and ESNs on a natural language task is therefore a natural choice for experimentation. Elman applies SRNs to a standard task in statistical NLP: predicting the next word in a corpus, given the previous words. Using a simple context-free grammar and an SRN with backpropagation through time (BPTT), Elman showed that the network was able to learn internal representations that were sensitive to linguistic processes that were useful for the prediction task. Here, using ESNs, we show that training such internal representations is unnecessary to achieve levels of performance comparable to SRNs. We also compare the processing capabilities of ESNs to bigrams and trigrams. Due to some unexpected regularities of Elman's grammar, these statistical techniques are capable of maintaining dependencies over greater distances than might be initially expected. However, we show that the memory of ESNs in this word-prediction task, although noisy, extends significantly beyond that of bigrams and trigrams, enabling ESNs to make good predictions of verb agreement at distances over which these methods operate at chance. Overall, our results indicate a surprising ability of ESNs to learn a grammar, suggesting that they form useful internal representations without learning them.

Dynamic parameter optimization of evolutionary computation for on-line prediction of time series with changing dynamics

This paper introduces a dynamic evolving computation system (DECS) model, for adaptive on-line learning, and its application for dynamic time series prediction. DECS evolve through evolving clustering method and evolutionary computation for structure learning, Levenberg–Marquardt method for parameter learning, learning and accommodate new input data. DECS is created and updated during the operation of the system. At each time moment the output of DECS is calculated through a knowledge rule inference system based on m-most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. An approach is proposed for a dynamic creation of a first order Takagi–Sugeno type fuzzy rule set for the DECS model. The fuzzy rules can be inserted into DECS before, or during its learning process, and the rules can also be extracted from DECS during, or after its learning process. It is demonstrated that DECS can effectively learn complex temporal sequences in an adaptive way and outperform some existing models.

DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction

This paper introduces a new type of fuzzy inference systems, denoted as dynamic evolving neural-fuzzy inference system (DENFIS), for adaptive online and offline learning, and their application for dynamic time series prediction. DENFIS evolve through incremental, hybrid (supervised/unsupervised), learning, and accommodate new input data, including new features, new classes, etc., through local element tuning. New fuzzy rules are created and updated during the operation of the system. At each time moment, the output of DENFIS is calculated through a fuzzy inference system based on m-most activated fuzzy rules which are dynamically chosen from a fuzzy rule set. Two approaches are proposed: (1) dynamic creation of a first-order Takagi-Sugeno-type fuzzy rule set for a DENFIS online model; and (2) creation of a first-order Takagi-Sugeno-type fuzzy rule set, or an expanded high-order one, for a DENFIS offline model. A set of fuzzy rules can be inserted into DENFIS before or during its learning process. Fuzzy rules can also be extracted during or after the learning process. An evolving clustering method (ECM), which is employed in both online and offline DENFIS models, is also introduced. It is demonstrated that DENFIS can effectively learn complex temporal sequences in an adaptive way and outperform some well-known, existing models.

Estimation Theory for Dynamic Systems with Unknown but Bounded Uncertainty: An Overview

In many problems such as linear and nonlinear regressions, parameter and state estimation of dynamic systems, state space and time series prediction, interpolation, smoothing, functions approximation, one has to evaluate some unknown variable using available data. The data are always associated with some uncertainty and it is necessary to evaluate how this uncertainty affects the estimated variables. Typically the problem is approached assuming a probabilistic description of uncertainty and applying statistical estimation theory. An interesting alternative approach, referred to as set membership or Unknown But Bounded (UBB) error description, has been investigated since the late 60's. In this approach, uncertainty is described by an additive noise which is known only to have given integral (typically l 2 or l 1 ) or componentwise ( l ∞ ) bounds. In this paper we review the main results of this theory, with special attention to the most recent advances obtained in the case of componentwise bounds.

Optimal estimation theory for dynamic systems with set membership uncertainty: An overview

In many problems such as linear and nonlinear regressions, parameter and state estimation of dynamic systems, state-space and time series prediction, interpolation, smoothing and functions approximation, one has to evaluate some unknown variable using available data. The data are always associated with some uncertainty and it is necessary to evaluate how this uncertainty affects the estimated variables. Typically, the problem is approached assuming a probabilistic description of uncertainty and applying statistical estimation theory. An interesting alternative approach, referred to as set membership or unknown but bounded (UBB) error description, has been investigated since the late 1960s. In this approach, uncertainty is described by an additive noise which is known only to have given integral (typically l 2 of l 1) or componentwise ( l ∞) bounds. In this paper we review the main results of this theory, with special attention to the most recent advances obtained in the case of componentwise bounds.