Adaptive prediction for stochastic processes with markov jump parameters

This paper is concerned with the adaptive prediction for stochastic processes with abruptly changing parameters modelled as a finite-state Markov chain. The Markov transition matrix is assumed to be known. For the coloured noise disturbance case, it is shown that the optimal prediction algorithm requires a bank of elemental predictors running in parallel with its number growing exponentially with time. If the noise disturbance is white, it is found that the number of the elemental predictors required increases exponentially with the prediction ahead step instead of time. A suboptimal predictor is proposed with substantial reduced storage and computational requirements. Simulation examples show the good performance of the proposed algorithms.     

电流连续模式下BUCK开关变换器的频率选取优化

针对BUCK开关变换器工作模态多样,影响效率因素众多等问题,通过对工作于电流连续模式（CCM）下的BUCK电路进行建模,对系统的开关损耗、传导损耗,以及其他杂散损耗进行了全面的分析。分析得出了开关频率、负载电流等因素与系统效率之间的关系。最后,根据分析结果,对开关频率进行优化选取。     

On estimation and prediction procedures for ar(1) models with power transformation

The power transformation of Box and Cox (1964) has been shown to be quite useful in short-term forecasting for the linear regression model with AR(1) dependence structure (see, for example, Lee and Lu, 1987, 1989). It is crucial to have good estimates of the power transformation and serial. correlation parameters, because they form the basis for estimating other parameters and predicting future observations. The prediction of future observations is the main focus of this paper. We propose to estimate these two parameters by minimizing the mean squared prediction errors. These estimates and the corresponding predictions compare favourably, via revs and simulated data, with those obtained by the maximum likelihood method. Similar results are also demonstrated in the repeated measurements setting.     

基于累积变形演化状态控制的高速铁路基床结构设计计算方法

实现长期循环荷载作用下基床结构累积变形有效控制是建造高速铁路的技术难点之一.基于高速铁路无砟与有砟轨道路基承受列车动荷载特征,提出和完善了用于基床结构设计的荷载作用模式;根据循环荷载作用下土工填料累积变形演化状态的分类及阈值判别准则,结合室内单元结构填土模型试验,获得了级配碎石基床填料变形状态演化的阈值参数.以设计荷载条件和填料设计参数为基础,针对无砟与有砟轨道对基床变形状态控制的不同要求,采用结构分析原理,构建了基于累积变形演化状态控制的高速铁路基床结构设计计算方法,确定了无砟与有砟轨道基床结构累积变形分别满足快速稳定和缓慢稳定的状态控制要求,以地基系数K30值表征的关键参数指标.研究成果为实现高速铁路基床结构由构造设计向状态控制定值分析转变奠定了基础.     

Smooth transition exponential smoothing

Adaptive exponential smoothing methods allow a smoothing parameter to change over time, in order to adapt to changes in the characteristics of the time series. However, these methods have tended to produce unstable forecasts and have performed poorly in empirical studies. This paper presents a new adaptive method, which enables a smoothing parameter to be modelled as a logistic function of a user‐specified variable. The approach is analogous to that used to model the time‐varying parameter in smooth transition models. Using simulated data, we show that the new approach has the potential to outperform existing adaptive methods and constant parameter methods when the estimation and evaluation samples both contain a level shift or both contain an outlier. An empirical study, using the monthly time series from the M3‐Competition, gave encouraging results for the new approach. Copyright © 2004 John Wiley & Sons, Ltd.     

基于能量平衡模型的陇东黄土高原蒸散发遥感反演——以平凉市崆峒区水土保持世行贷款项目区为例

利用TM遥感影像和地表热量平衡模型估算平凉市崆峒区水土保持世行贷款项目区的蒸散发量,并结合地面实测资料进行检验,分析了该区域蒸散发的分布规律,研究了蒸散发量与土地利用、地表参数、地形参数的关系。结果表明：研究区当日蒸散发量介于0．46～7．85mm之间,平均3．81mm,空间分布差异明显;不同下垫面的蒸散发能力有一定差别,其中水体和林地的日蒸散发量最大;蒸散发量与NDVI和斋程等呈线性正相关,而与地表温度呈线性负相关。本研究可为区域地表水循环和生态修复提供数据支持。     

Forecasting ski demand: comparing learning curve and varying parameter coefficient approaches

Demand for skiing expanded rapidly in the 1980s, fell quite dramatically at the start of the 1990s as the economy declined but has not subsequently recovered. Two possible explanations are explored. The first is based on perceiving skiing as a new product to most consumers, which reached maximum growth in 1989. Current levels now largely represent ‘repeat buyers’. The alternative approach sees the growth as the result of economic factors, particularly credit conditions. The importance of these factors was not, however, constant, and grew with the changes in the financial system. Thus the recovery had a muted effect. These two approaches are modelled, estimated and the results compared by both residual and ex post forecasting analysis. The paper concludes that the varying coefficient econometric model probably produces the most reliable forecasts. Copyright © 1999 John Wiley & Sons, Ltd.     

Restructuring performance prediction with a rebalanced and clustered support vector machine

This paper discusses whether asset restructuring can improve firm performance over decades. Variation in the stock price or the financial ratio is used as the dependent variable of either short‐ or long‐term effectiveness to evaluate the variance both before and after asset restructuring. The result is varied. It is necessary to develop a foresight approach for the mixed situation. This work pioneers to forecast effectiveness of asset restructuring with a rebalanced and clustered support vector machine (RCS). The profitability variation 1 year before and after asset restructuring is used as the dependent variable. The current financial indicators of the year of asset restructuring are used as independent variables. Specially treated listed companies are used as research samples, as they frequently adopt asset restructuring. In modeling, the skew distribution of samples achieving and failing to achieve performance improvement with asset restructuring is handled with rebalancing. The similar experienced knowledge of asset restructuring to the current asset restructuring is filtered out with clustering. With the help from rebalancing and clustering, a support vector machine is constructed for prediction, together with other forecasting models of multivariate discriminant analysis, logistic regression, probit regression, and case‐based reasoning. These models' standalone modes are used as benchmarks. The empirical results demonstrate the applicability of the RCS for forecasting effectiveness of asset restructuring.     

高k栅MOSFET栅–源/漏寄生电容的半解析模型

文章针对高k栅MOSFET的栅介质层及其侧壁掩蔽层提出了一个二维定解问题,求出了二维电势和电荷分布.文章根据栅极电荷与栅源及栅漏电压关系,提出了MOSFET的栅极和源极/漏极之间的寄生电容的模型,用半解析法计算了这些寄生电容,得到了寄生电容与几何尺寸之间的关系.文章的计算结果表明改变栅极电介质常数可以得到一个寄生电容的最小值,计算结果与CST仿真结果能够很好地符合.     

Backward‐in‐Time Selection of the Order of Dynamic Regression Prediction Model

We investigate the optimal structure of dynamic regression models used in multivariate time series prediction and propose a scheme to form the lagged variable structure called Backward‐in‐Time Selection (BTS), which takes into account feedback and multicollinearity, often present in multivariate time series. We compare BTS to other known methods, also in conjunction with regularization techniques used for the estimation of model parameters, namely principal components, partial least squares and ridge regression estimation. The predictive efficiency of the different models is assessed by means of Monte Carlo simulations for different settings of feedback and multicollinearity. The results show that BTS has consistently good prediction performance, while other popular methods have varying and often inferior performance. The prediction performance of BTS was also found the best when tested on human electroencephalograms of an epileptic seizure, and for the prediction of returns of indices of world financial markets.Copyright © 2013 John Wiley & Sons, Ltd.     

Assessing inefficiency in the s&p 500 futures market

We analyse the price movement of the S&P 500 futures market for violations of the efficient market hypothesis on a short-term basis. To assess market inefficiency we construct a model and find that the returns, i.e. the difference in the logarithm of closing prices on consecutive days, exhibit the usual conditional heteroscedasticity behaviour typical of long series of financial data. To account for this non-linear behaviour we scale the returns by a volatility factor which depends on the daily high, low, and closing price. The rescaled series, which may be interpreted as the trend-countertrend component of the time series, is modelled using Box and Jenkins techniques. The resulting model is an ARMA(1,1). The scale factors are assumed to form a time series and are modelled using a semi-non-parametric method which avoids the restrictive assumptions of most ARCH or GARCH models. Using the combined model we perform 1000 simulations of market data, each simulation comprising 250 days (approximately one year). We then formulate a naive trading strategy which is based on the ratio of the one-day-ahead expected return to its one-day-ahead expected conditional standard deviation. The trading strategy has four adjustable parameters which are set to maximize profits for the simulation data. Next, we apply the trading strategy to one year of recent out-of-sample data. Our conclusion is that the S&P 500 futures market exhibits only slight inefficiencies, but that there exist, in principle, better trading strategies which take account of risk than the benchmark strategy of buy-and-hold. We have also constructed a linear model for the return series. Using the linear model, we have simulated returns and determined the optimum values for the adjustable parameters of the trading strategy. In this case, the optimum trading strategy is the same as the benchmark strategy, buy-and-hold. Finally, we have compared the profitability of the optimized trading strategy, based on the non-linear model, to three ad hoc trading strategies using the out-of-sample data. The three ad hoc strategies are more profitable than the optimized strategy.     

Can panel data really improve the predictability of the monetary exchange rate model?

A common explanation for the inability of the monetary model to beat the random walk in forecasting future exchange rates is that conventional time series tests may have low power, and that panel data should generate more powerful tests. This paper provides an extensive evaluation of this power argument to the use of panel data in the forecasting context. In particular, by using simulations it is shown that although pooling of the individual prediction tests can lead to substantial power gains, pooling only the parameters of the forecasting equation, as has been suggested in the previous literature, does not seem to generate more powerful tests. The simulation results are illustrated through an empirical application. Copyright © 2007 John Wiley & Sons, Ltd.     

Combining var estimation and state space model reduction for simple good predictions

A combination of VAR estimation and state space model reduction techniques are examined by Monte Carlo methods in order to find good, simple to use, procedures for determining models which have reasonable prediction properties. The presentation is largely graphical. This helps focus attention on the aspects of the model determination problem which are relatively important for forecasting. One surprising result is that, for prediction purposes, knowledge of the true structure of the model generating the data is not particularly useful unless parameter values are also known. This is because the difficulty in estimating parameters of the true model causes more prediction error than results from a more parsimonious approximate model.     

Combination of forecasts in electricity demand prediction

Accurate demand prediction is of great importance in the electricity supply industry. Electricity cannot be stored, and generating plant must be scheduled well in advance to meet future demand. Up to now, where online information about external conditions is unavailable, time series methods on the historical demand series have been used for short-term demand prediction. These have drawbacks, both in their sensitivity to changing weather conditions and in their poor modelling of the daily/weekly business cycles. To overcome these problems a framework has been constructed whereby forecasts from different prediction methods and different forecasting origins can be selected and combined, solely on the basis of recent forecasting performance, with no a priori assumptions of demand behaviour. This added flexibility in univariate forecasting provides a significant improvement in prediction accuracy.     

Business failure prediction using decision trees

Accurate business failure prediction models would be extremely valuable to many industry sectors, particularly financial investment and lending. The potential value of such models is emphasised by the extremely costly failure of high‐profile companies in the recent past. Consequently, a significant interest has been generated in business failure prediction within academia as well as in the finance industry. Statistical business failure prediction models attempt to predict the failure or success of a business. Discriminant and logit analyses have traditionally been the most popular approaches, but there are also a range of promising non‐parametric techniques that can alternatively be applied. In this paper, the relatively new technique of decision trees is applied to business failure prediction. The numerical results suggest that decision trees could be superior predictors of business failure as compared to discriminant analysis. Copyright © 2009 John Wiley & Sons, Ltd.     

Short term load forecasting in electric power systems: A comparison of ARMA models and extended wiener filtering

On-line prediction of electric load in the buses of the EHV grid of a power generation and transmission system is basic information required by on-line procedures for centralized advanced dispatching of power generation. This paper presents two alternative approaches to on-line short term forecasting of the residual component of the load obtained after the removal of the base load from a time series of total load. The first approach involves the use of stochastic ARMA models with time-varying coefficients. The second consists in the use of an extension of Wiener filtering due to Zadeh and Ragazzini. Real data representing a load process measured in an area of Northern Italy and simulated data reproducing a non-stationary process with known characteristics constitute the basis of a numerical comparison allowing one to determine under which conditions each method is more appropriate.     

基于粗糙集与灰色动态预测模型在电力负荷短期预测的应用

针对电力系统多因素负荷预测问题的复杂性,结合粗糙集理论与GM（1,N）模型各自的优势,提出一种基于粗糙集理论的GM（1,N）预测模型.采取粗糙集理论对影响负荷预测因素进行简约,利用GM（1,N）建立简约后的因素变量和负荷之间的关系建立模型,并与GM（1,1）预测模型进行了比较,结果反映基于粗糙集理论的GM（1,N）预测模型的优越性,精准度达到94.055%.     

Starch-binding domains in the post-genome era

Starch belongs to the most abundant biopolymers on Earth. As a source of energy, starch is degraded by a large number of various amylolytic enzymes. However, only about 10% of them are capable of binding and degrading raw starch. These enzymes usually possess a distinct sequence-structural module, the so-called starchbinding domain (SBD). In general, all carbohydrate-binding modules (CBMs) have been classified into the CBM families. In this sequence-based classification the individual types of SBDs have been placed into seven CBM families: CBM20, CBM21, CBM25, CBM26, CBM34, CBM41 and CBM45. The family CBM20, known also as a classical C-terminal SBD of microbial amylases, is the most thoroughly studied. The three-dimensional structures have already been determined by X-ray crystallography or nuclear magnetic resonance for SBDs from five CBM families (20, 25, 26, 34 and 41), and the structure of the CBM21 has been modelled. Despite differences among the amino acid sequences, the fold of a distorted β-barrel seems to be conserved together with a similar way of substrate binding (mainly stacking interactions between aromatic residues and glucose rings). SBDs have recently been discovered in many non-amylolytic proteins. These may, for example, have regulatory functions in starch metabolism in plants or glycogen metabolism in mammals. SBDs have also found practical uses. Received 25 May 2006; received after revision 26 June 2006; accepted 3 August 2006     

Metering and modelling residential end-use electricity load curves

In most electricity systems the residential sector is one of the main contributors to the system peak. This makes it important to know how different residential end uses, such as space heating or cooking, contribute to the system load curve at the time of system peak and also at other times of the day. In this paper we discuss the estimation of residential end-use load curves for the state of New South Wales in Australia. Half-hourly readings were taken for 15 months on the total load and a range of end-use loads of 250 households. Information was sought on 16 different end uses, while eight metering channels were available for each household. We describe the optimal design procedure used to determine which end uses to meter in each household. The econometric model used for estimating the end-use load curves integrates a conditional demand analysis (CDA) of the total load readings for the household with the readings on all the directly metered end uses. Our integrated approach achieves impressive gains in efficiency over the conventional approach to estimating end-use loads. The paper concludes with an illustration of how end-use load curves can be used to simulate a variety of policy options.     

Time series multistep‐ahead predictability estimation and ranking

A predictability index was defined as the ratio of the variance of the optimal prediction to the variance of the original time series by Granger and Anderson (1976) and Bhansali (1989). A new simplified algorithm for estimating the predictability index is introduced and the new estimator is shown to be a simple and effective tool in applications of predictability ranking and as an aid in the preliminary analysis of time series. The relationship between the predictability index and the position of the poles and lag p of a time series which can be modelled as an AR(p) model are also investigated. The effectiveness of the algorithm is demonstrated using numerical examples including an application to stock prices. Copyright © 1999 John Wiley & Sons, Ltd.