A short-term adaptive forecasting model for BIFFEX speculation: a Box—Jenkins approach

There are numerous possible advantages to be gained from the accurate prediction of future movements in the Baltic Freight Index (BFI). Because of the difficulties inherent in long-range forecasting, however, the potential for such predictions to provide insight into the future state of the physical dry bulk market is perhaps limited. The greater accuracy of short-term forecasts, on the other hand, facilitates the development of a forecasting model form is justified by the inevitably continuous nature of futures market speculation. Such a model is developed through the application of the Box—Jenkins approach to time series analysis and forecasting. The methodology is presented and the resulting model is evaluated on the basis of objective measures of predictive power and by comparison with alternative forecasting models. Finally, the applicability of the model to the practice of BIFFEX speculation is assessed by judging its performance within a simulated BIFFEX trading environment.     

The question of safety

There are numerous possible advantages to be gained from the accurate prediction of future movements in the Baltic Freight Index (BFI). Because of the difficulties inherent in long-range forecasting, however, the potential for such predictions to provide insight into the future state of the physical dry bulk market is perhaps limited. The greater accuracy of short-term forecasts, on the other hand, facilitates the development of a forecasting model form is justified by the inevitably continuous nature of futures market speculation. Such a model is developed through the application of the Box—Jenkins approach to time series analysis and forecasting. The methodology is presented and the resulting model is evaluated on the basis of objective measures of predictive power and by comparison with alternative forecasting models. Finally, the applicability of the model to the practice of BIFFEX speculation is assessed by judging its performance within a simulated BIFFEX trading environment.     

Operational surface drift prediction using linear and non-linear hyper-ensemble statistics on atmospheric and ocean models

Multimodel super-ensemble forecasts, which exploit the power of an optimal local combination of individual models usually show superior forecasting skills when compared to individual models because they allow for local correction and/or bias removal. Deterministic approaches to the problem of surface drift are often limited by strong assumptions on the underlying physics. A new approach based on linear and non-linear optimization is proposed, using hyper-ensemble deduced statistics to forecast at short time scale Lagrangian drifts from combined atmospheric and ocean operational models and local observations that were made available during the MREA04 field experiment along the West coast of Portugal. Optimization methods are based on a training/forecast cycle. The performance and the limitations of the hyper-ensembles and the individual models are discussed. Results suggest that our statistical methods reduce the position errors significantly for 12 to 48 h forecasts and hence compete with pure deterministic approaches.     

Forecasting wind with neural networks

Wind forecasts over a varying period of time are needed for a variety of applications in the coastal and ocean region, like planning of construction and operation-related works as well as prediction of power output from wind turbines located in coastal areas. Such forecasting is currently done by adopting complex atmospheric models or by using statistical time-series analysis. Because occurrence of wind in nature is extremely uncertain no single technique can be entirely satisfactory. This leaves scope for alternative approaches. The present work employs the technique of neural networks in order to forecast daily, weekly as well as monthly wind speeds at two coastal locations in India. Both feed forward as well as recurrent networks are used. They are trained based on past data in an auto-regressive manner using back-propagation and cascade correlation algorithms. A generally satisfactory forecasting as reflected in its higher correlation and lower deviations with actual observations is noted. The neural network forecasting is also found to be more accurate than traditional statistical time-series analysis.     

On-line wave prediction

Operational prediction of wave heights is generally made with the help of complex numerical models. This paper presents alternative schemes based on stochastic and neural network approaches. First order auto regressive moving average and auto regressive integrated moving average type of models along with a three-layered feed forward network are considered. The networks are trained using three different algorithms to make sure of the correct training. Predictions over intervals of 3, 6, 12 and 24 h are made at an offshore location in India where 3-hourly wave height data were being observed. Comparison of model predictions with the actual observations showed generally satisfactory performance of the chosen tools. Neural networks made more accurate predictions of wave heights than the time series schemes when shorter intervals of predictions were involved. For long range predictions both the stochastic and neural approaches showed similar performance. Small interval predictions were made more accurately than the large interval ones.     

Forecasting front displacements with a satellite based ocean forecasting (SOFT) system

Relatively long term time series of satellite data are nowadays available. These spatio–temporal time series of satellite observations can be employed to build empirical models, called satellite based ocean forecasting (SOFT) systems, to forecast certain aspects of future ocean states. The forecast skill of SOFT systems predicting the sea surface temperature (SST) at sub-basin spatial scale (from hundreds to thousand kilometres), has been extensively explored in previous works. Thus, these works were mostly focussed on predicting large scale patterns spatially stationary. At spatial scales smaller than sub-basin (from tens to hundred kilometres), spatio–temporal variability is more complex and propagating structures are frequently present. In this case, traditional SOFT systems based on Empirical Orthogonal Function (EOF) decompositions could not be optimal prediction systems. Instead, SOFT systems based on Complex Empirical Orthogonal Functions (CEOFs) are, a priori, better candidates to resolve these cases.In this work we study and compare the performance of an EOF and CEOF based SOFT systems forecasting the SST at weekly time scales of a propagating mesoscale structure. The SOFT system was implemented in an area of the Northern Balearic Sea (Western Mediterranean Sea) where a moving frontal structure is recurrently observed. Predictions from both SOFT systems are compared with observations and with the predictions obtained from persistence models. Results indicate that the implemented SOFT systems are superior in terms of predictability to persistence. No substantial differences have been found between the EOF and CEOF-SOFT systems.     

组合预测模型在上海内河港口吞吐量预测中的应用

建立时间序列和二元线性回归的组合预测模型,对上海内河港口2010年、2015年和2020年的货物吞吐量水平进行了预测。研究发现,组合预测模型相比单个预测方法具有较高的精度,能够较准确地预测上海内河港口货物吞吐量。     

Bayesian statistical data assimilation for ecosystem models using Markov Chain Monte Carlo

This study considers advanced statistical approaches for sequential data assimilation. These are explored in the context of nowcasting and forecasting using nonlinear differential equation based marine ecosystem models assimilating sparse and noisy non-Gaussian multivariate observations. The statistical framework uses a state space model with the goal of estimating the time evolving probability distribution of the ecosystem state. Assimilation of observations relies on stochastic dynamic prediction and Bayesian principles. In this study, a new sequential data assimilation approach is introduced based on Markov Chain Monte Carlo (MCMC). The ecosystem state is represented by an ensemble, or sample, from which distributional properties, or summary statistical measures, can be derived. The Metropolis-Hastings based MCMC approach is compared and contrasted with two other sequential data assimilation approaches: sequential importance resampling, and the (approximate) ensemble Kalman filter (including computational comparisons). A simple illustrative application is provided based on a 0-D nonlinear plankton ecosystem model with multivariate non-Gaussian observations of the ecosystem state from a coastal ocean observatory. The MCMC approach is shown to be straightforward to implement and to effectively characterize the non-Gaussian ecosystem state in both nowcast and forecast experiments. Results are reported which illustrate how non-Gaussian information originates, and how it can be used to characterize ecosystem properties.     

考古船阻力计算中湍流模型与格式研究

为了研究不同湍流模型在船舶水动力性能预报中的适用性,在求解RANS方程的数值计算方法过程中,通过采用S-A、k-Omega、SST、EASM、DES等湍流模型,对考古船的阻力进行了预报,并将计算结果与试验结果进行了对比分析。由5种湍流模型的预报结果与试验值的对比得出：S-A、k-Omega模型对阻力性能的预报存在明显的缺陷;SST模型会有改进,计算误差能控制在工程应用的范围内,能够很好地应用于船模的阻力计算。     

基于LSTM的舰船运动姿态短期预测

舰船的六自由度运动状态形成复杂的非线性过程,运动姿态会受到耦合作用、不定周期、噪声信号以及混沌特性等因素的干扰,因此很难得到精确的预测结果.为了提升舰船运动姿态的预测精度,利用舰船时间序列的特点,建立了基于长短期记忆单元(LSTM)模型,对其进行了舰船姿态预测仿真,将结果与时间序列分析法的结果进行对比.实例分析表明:基于LSTM模型的预测方法具有精确度高、易实现的特点.这为舰船运动短期预测提供了一个新的思路和方法.     

基于组合预测模型的世界海运周转量预测分析

世界海运周转量是衡量未来航运市场运力需求的直接体现,在确定航运市场和船舶市场的发展趋势方面具有关键作用。针对世界海运周转量受到众多复杂因素影响的现实,基于传统的单个预测方法,分别采用时间序列、灰色系统、神经网络方法对世界海运周转量进行预测,然后再对单个预测方法进行加权组合,建立组合预测模型进行海运周转量的预测,预测结果表明:组合预测模型能够得到更加可靠的结果。     

基于条件期望的港口货物吞吐量预测模型的建立与分析

为了有效预测港口货物总吞吐量的大小,利用条件数学期望提出了港口货物总吞吐量的预测模型.由于货物总吞吐量的变化与到达港口的货运船数目以及装卸设备的工作效率有密切关系,构造一个关于到达港口的货运船数目以及装卸设备的工作能力组合而成的复合变量,货物总吞吐量是这些复合变量所表示的货物装卸量的和.应用全概理论,得到货物总吞吐量的概率分布.在此基础上,将未来货物总吞吐量看作已完成吞吐量的条件期望.利用增长函数得出港口货物吞吐量的预测模型.以山东地区某港口的货物吞吐量变化规律进行了案例分析.理论分析和案例分析均表明该模型是预测港口货物总吞吐量的有效方法.     

组合预测在港口吞吐量预测中的应用研究

以某港口1996—2002年吞吐量为原始数据,按照“误差平方和最小”的准则,把一元线性回归模型和GM(1,1)模型组合起来,对某港口2004—2008年的吞吐量进行了组合预测。     

Using the SKAGEX dataset for evaluation of ocean model skills

Numerical ocean models are now being applied in numerous oceanographic studies. However, the qualities of the model results are often uncertain and there is a great need for standards and procedures for evaluation of the skills of numerical general circulation models. In this paper measurements from repeated hydrographical sections across Skagerrak taken in 1990, the SKAGEX dataset, are used to evaluate the skills of two σ-coordinate ocean models and to study the sensitivity of these models to model parameters. A methodology for quantification of model skills based on observations from repeated hydrographical sections in general is suggested. Area averages of absolute differences are for Skagerrak completely dominated by the discrepancies in the upper few meters of the ocean and may not be used to assess models' abilities to reproduce the fields in the larger and deeper part of the ocean. Therefore, discrepancies between average values in time from the observed fields and time averaged values from model outputs are related to the natural variability of the fields. The numbers produced with the suggested measure are relative numbers that will be specific for each section and for each series of observation. Ideally we would therefore like to see the measures computed for a number of sections for various models and choices of model parameters in order to assess model skills. The value of the SKAGEX dataset as a tool for model improvements is demonstrated. Evidence to support the importance of applying non-oscillatory, gradient preserving advection schemes in areas with sharp density fronts is given. The method is used to identify that the forcing/initial values/boundary values for the temperature field are inferior to the corresponding values for the salinity field. With the present coarse resolution, 11 layers in the vertical, it is shown that it is far from obvious that the quality of the model results improve when replacing simple Richardson number formulations for vertical mixing processes with higher order turbulence closure in the Skagerrak area.     

神经网络模型在港口吞吐量预测中的应用与误差分析

应用神经网络BP算法对杭州港的吞吐量预测实例进行了详细分析。通过对网络各种参数的调试与组合得出,当隐含层节点数为15,训练控制误差为0.035,分级迭代级数为4级,平滑因子参数为0.2,学习速率参数为1.5时,网络性能最佳。将网络预测结果与时间序列和回归分析2种方法进行了比较,得出神经网络方法在短期预测中要优于传统方法。通过对模型预测误差产生原因的简要分析,得出神经网络方法并不适用于吞吐量长期预测。最后对其应用过程中可能存在的一些问题提出了建议。     

指数平滑模型探讨

指数平滑模型在经济预测中应用较为广泛,时间序列的变动趋势不同,应用的平滑模型有所区别。本文介绍了各种指数平滑模型,并以某港为例分别用三次指数平滑及二阶差分—指数平滑模型对其吞吐量进行了预测,并做出了各种模型的适用性分析。     

改进的BP神经网络在船舶与海洋工程中的应用研究

人工神经网络作为一个具有高度非线性映射能力的计算模型,在工程中具有广泛的应用前景.在数值预测方面,它不需要预选确定样本的数学模型,仅通过学习样本数据即可以进行预测.文章介绍了BP神经网络,并针对实际应用中收敛速度慢,平台效应等问题对网络进行了改进并优化,详尽地给出了改进的三层BP神经网络数值预测算法.为测试该算法.选用了著名的XOR(异或)问题和和一个高度非线性的0-1矩阵预测问题对其进行了验证.计算结果表明文中算法能给出令人满意的精度.最后结合船舶与海洋工程的两个实际问题,探讨了利用改进的BP神经网络进行数值预测的方法和应该注意的问题,并给出了一些有益的建议.实践表明,文中给出的改进的BP神经网络数值预测算法值得在船舶与海洋工程中加以应用并推广.     

Wave tranquility studies using neural networks

Information on heights of waves and their distribution around harbor entrances is traditionally obtained from the knowledge of incident wave, seabed and harbor characteristics by using experimental as well as numerical models. This paper presents an alternative to these techniques based on the computational tool of neural networks. Modular networks were developed in order to estimate wave heights in and around a dredged approach channel leading to harbor entrance. The data involved pertained to two harbor locations in India. The training of networks was done using a numerical model, which solved the mild slope equation. Test of the network with several alternative error criteria confirmed capability of the neural network approach to perform the wave tranquility studies. A variety of learning schemes and search routines were employed so as to select the best possible training to the network. Mutual comparison between these showed that the scaled conjugate method was the fastest among all whereas the one step secant scheme was the most memory efficient. The Brent's search and the golden section search routines forming part of the conjugate gradient Fletcher–Reeves update approach of training took the least amount of time to train the network per epoch. Calibration of the neural network with both mean square as well as the sum squared error as performance functions yielded satisfactory results.     

船舶在波浪中阻力增加预报研究进展

从数值计算和模型试验两个方面,介绍船舶在波浪中阻力增加预报的研究进展,分析各种方法的基本原理,提出阻力增加预报的发展方向.