Short‐term salmon price forecasting

This study establishes a benchmark for short‐term salmon price forecasting. The weekly spot price of Norwegian farmed Atlantic salmon is predicted 1–5 weeks ahead using data from 2007 to 2014. Sixteen alternative forecasting methods are considered, ranging from classical time series models to customized machine learning techniques to salmon futures prices. The best predictions are delivered by k‐nearest neighbors method for 1 week ahead; vector error correction model estimated using elastic net regularization for 2 and 3 weeks ahead; and futures prices for 4 and 5 weeks ahead. While the nominal gains in forecast accuracy over a naïve benchmark are small, the economic value of the forecasts is considerable. Using a simple trading strategy for timing the sales based on price forecasts could increase the net profit of a salmon farmer by around 7%.     

A Method on Non-Linear Correction of Broadband LFMCW Signal Utilizing Its Relative Sweep Non-Linear Error

1. INTaODUCTIONLinear frequency modulation continuous wave (LFMCW) radar with broadband has a high range Precisinlland high resollltion because of its large time bandwidth product and has no blind range at the'sarne take.LFMCW radax has more extensive applications in many fields such as microwave ranging, crashworthy car, andmissile's terminal guidance because broadband LFMCW devices are being miniaturized and integrated with therapid developmellt of microwave and millimeter wave ill…     

Forecast robustness in macroeconometric models

This paper investigates potential invariance of mean forecast errors to structural breaks in the data generating process. From the general forecasting literature, such robustness is expected to be a rare occurrence. With the aid of a stylized macro model we are able to identify some economically relevant cases of robustness and to interpret them economically. We give an interpretation in terms of co‐breaking. The analytical results resound well with the forecasting record of a medium‐scale econometric model of the Norwegian economy.     

Bias‐corrected bootstrap prediction intervals for autoregressive model: new alternatives with applications to tourism forecasting

This paper proposes the use of the bias‐corrected bootstrap for interval forecasting of an autoregressive time series with an arbitrary number of deterministic components. We use the bias‐corrected bootstrap based on two alternative bias‐correction methods: the bootstrap and an analytic formula based on asymptotic expansion. We also propose a new stationarity‐correction method, based on stable spectral factorization, as an alternative to Kilian's method exclusively used in past studies. A Monte Carlo experiment is conducted to compare small‐sample properties of prediction intervals. The results show that the bias‐corrected bootstrap prediction intervals proposed in this paper exhibit desirable small‐sample properties. It is also found that the bootstrap bias‐corrected prediction intervals based on stable spectral factorization are tighter and more stable than those based on Kilian's stationarity‐correction. The proposed methods are applied to interval forecasting for the number of tourist arrivals in Hong Kong. Copyright © 2010 John Wiley & Sons, Ltd.     

Beating the VAR: Improving Swedish GDP Forecasts Using Error and Intercept Corrections

This paper examines the forecast accuracy of an unrestricted vector autoregressive (VAR) model for GDP, relative to a comparable vector error correction model (VECM) that recognizes that the data are characterized by co‐integration. In addition, an alternative forecast method, intercept correction, is considered for further comparison. Recursive out‐of‐sample forecasts are generated for both models and forecast techniques. The generated forecasts for each model are objectively evaluated by a selection of evaluation measures and equal accuracy tests. The result shows that the VECM consistently outperforms the VAR models. Further, intercept correction enhances the forecast accuracy when applied to the VECM, whereas there is no such indication when applied to the VAR model. For certain forecast horizons there is a significant difference in forecast ability between the intercept corrected VECM compared to the VAR model. Copyright © 2015 John Wiley & Sons, Ltd.     

准同期参数测量算法研究

在发电机并网过程中,对信号的频率、相位、幅值等参数要进行快速、准确的测量,传统的测量方法参数误差比较大.本文提出利用窗函数及频谱校正的DFT算法来对信号进行参数测量,仿真结果表明,该算法不需要对电压信号进行整周期采样,可以对频率在较大范围内变化的信号进行准确测量,从而较好的解决了由于并列双方电压的频率不断变化而引起的测量误差大的问题.     

一个计算行星轮对定轴动量矩的新方法

推导了行星轮的运动分解为随转动参考系的转动和相对于转动参考系的转动时,计算其对定轴动量矩的一个新方法.该方法表明,行星轮对定轴动量矩等于牵连动量矩与相对动量矩之和,其物理意义直观明确,应用方便.     

基于Hough变换的列车客运票图像倾斜校正

为解决列车客运票防伪和自动验票中的列车客运票图像倾斜校正的问题,提出了一种基于Hough变换的列车客运票图像倾斜校正方法.该方法根据现有倾角检测算法及列车客运票图像的特点,将列车客运票图像的前景与背景分离,在对图像前景进行水平轨迹线平滑的基础上提取文本行特征线,然后采用分级Hough变换的思想实现倾角的检测,最后用直线拟合的方法实现图像的校正.实验结果表明了所提出的算法的有效性和可靠性.     

论高校图书馆对学生读者不良行为的矫正

论述了高校图书馆对学生读者不良行为矫正发生教育作用的心理学机制,分析了影响矫正教育效果的各种因素,并提出了相应的措施。     

分形导体粗糙表面的后向增强效应分析

针对粗糙表面散射实验中的后向散射增强现象,采用锥形波束入射的矩量法定量计算了分形粗糙表面的后向散射增强效应,研究了波形参数和表面尺寸的匹配问题,分析了不同入射角下散射增强的角宽度,比较了不同分维数和表面模型下散射增强的幅值。数值计算结果证明了该算法的有效性。