bluuebunny/crossref_metadata_2025 huggingface dataset:
Structural change and the combination of forecasts
Authors: Diebold Francis X. , Pauly Peter
Published: July 2007

Forecasters are generally concerned about the properties of model‐based predictions in the presence of structural change. In this paper, it is argued that forecast errors can under those conditions be greatly reduced through systematic combination of forecasts. We propose various extensions of the standard regression‐based theory of forecast combination. Rolling weighted least squares and time‐varying parameter techniques are shown to be useful generalizations of the basic framework. Numerical examples, based on various types of structural change in the constituent forecasts, indicate that the potential reduction in forecast error variance through these methods is very significant. The adaptive nature of these updating procedures greatly enhances the effect of risk‐spreading embodied in standard combination techniques.
State‐space modelling of residential, commercial and peak demands
Authors: Nelson Robert
Published: July 2007

The potential use of state‐space modelling is evaluated through comparison with the existing multivariate ARMA models currently in use at Georgia Power Company for forecasting its residential sales, commercial sales and peak demand.
Forecasts of power‐transformed series
Authors: Pankratz Alan , Dudley Underwood
Published: July 2007

Consider a time series transformed by an instantaneous power function of the Box‐Cox type. For a wide range of fractional powers, this paper gives the relative bias in original metric forecasts due to use of the simple inverse retransformation when minimum mean squared error (conditional mean) forecasts are optimal. This bias varies widely according to the characteristics of the data. A fast algorithm is given to find this bias, or to find minimum mean squared error forecasts in the original metric. The results depend on the assumption that the forecast errors in the transformed metric are Gaussian. An example using real data is given.
Outliers, level shifts, and variance changes in time series
Authors: Tsay Ruey S.
Published: July 2007

Outliers, level shifts, and variance changes are commonplace in applied time series analysis. However, their existence is often ignored and their impact is overlooked, for the lack of simple and useful methods to detect and handle those extraordinary events. The problem of detecting outliers, level shifts, and variance changes in a univariate time series is considered. The methods employed are extremely simple yet useful. Only the least squares techniques and residual variance ratios are used. The effectiveness of these simple methods is demonstrated by analysing three real data sets.
A new method of scenario analysis for strategic planning
Authors: Brauers Jutta , Weber Martin
Published: July 2007

We first present scenario analysis as a qualitative forecasting technique useful for strategic planning. Then we develop an overview of the two classes of methods for scenario analysis described in the literature. Based on both classes, a new method is developed which especially fits the needs of strategic planning. The method can be divided into three stages: 1. Determination of compatible scenarios, 2. Determination of scenario probabilities, and 3. Determination of main scenarios. An example is given to illustrate the method.
Adaptive prediction for stochastic processes with markov jump parameters
Authors: Xiao‐Jiang Qi
Published: July 2007

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.
A dynamic linear model approach for disaggregating time series data
Authors: Al‐Osh M.
Published: July 2007

An approach is proposed for obtaining estimates of the basic (disaggregated) series, xi, when only an aggregate series, yt, of k period non‐overlapping sums of xi's is available. The approach is based on casting the problem in a dynamic linear model form. Then estimates of xican be obtained by application of the Kalman filtering techniques. Anad hocprocedure is introduced for deriving a model form for the unobserved basic series from the observed model of the aggregates. An application of this approach to a set of real data is given.
Forecasting with more than one model
Authors: Bunn Derek
Published: July 2007

This paper comprises an editorial review for the Special Issue on Combining Forecasts. It gives a background to the current growth of interest in this topic and speculates upon some of the reasons for this popularity. Some of the main methodological issues in practice are also described.
Invited review combining forecasts—twenty years later
Authors: Granger C. W. J.
Published: July 2007

The combination of forecasts is a simple, pragmatic and sensible way to possibly produce better forecasts. Simple extensions of the original idea involve the use of various available ‘forecasts’ even if some are rather inefficient. Some unsolved questions relate to combining forecasts with horizons longer than one period. More complicated extensions are associated with ‘encompassing’ and the combination of confidence intervals or quantiles. The relevance of information sets is emphasized in both the underlying theory and the interpretation of combinations.
N‐step combinations of forecasts
Authors: Gunter Sevket I. , Aksu Celal
Published: July 2007

While there is general agreement that a linear combination of forecasts can outperform the individual forecasts, there is controversy about the appropriateness of the combination method to be used in a given situation. Hence, in any given application it may be more beneficial to combine different sets of combined forecasts rather than picking one of them. This paper introduces the concept of N‐step combinations of forecasts which involves combining the combined forecasts obtained from different combination procedures used at the preceding step. Using quarterly GNP data, evidence supporting the increase in the accuracy of the one‐period‐ahead ex‐ante forecasts as the combination step increases is provided. The MSE, MAE, MAPE and their corresponding standard deviations are used to evaluate the accuracy of the forecasts obtained.
Composite model building for foreign exchange rates
Authors: Guerard John B.
Published: July 2007

It has been shown in recent economic and statistical studies that composite forecasts may produce more accurate forecasts than individual ones. The purpose of this study is to develop composite forecasting models that may produce forecasts superior to the individual forecast implicit in forward exchange rates. In an efficient market one would expect to find little improvement with the composite models relative to the forward exchange rate.
A comparison of tests for setar‐type non‐linearity in time series
Authors: Petruccelli Joseph D.
Published: July 2007

Tests for SETAR‐type non‐linearity in time series have recently been proposed by Petruccelli and Davies (1986), W. S. Chan and Tong (1986), Tsay (1987), Luukkonenet al.(1988), Petruccelli (1987) and Moeanaddin and Tong (1988). In this paper we consider the relative performance of thes: tests.
Decomposition; a strategy for judgemental forecasting
Authors: Edmundson R. H.
Published: July 2007

This paper reports the results of studies concerning the accuracy and efficiency of time‐series extrapolation decisions made with the assistance of an interactive graphical tool called GRAFFECT. The tool facilitates the decomposition of the extrapolation task by permitting the serial decomposition of the cue data as the task proceeds. GRAFFECT uses an interactive graphical interface controlled substantially with the use of a mouse. The extrapolation task is divided into the following: (1) trend modelling and extrapolation, (2) seasonal pattern modelling, and (3) extrapolation from the noise residual series. As each component is modelled its effect is stored and the information is washed out of the cue series. The ultimate forecast is produced by automatic recomposition of the judgementally determined components. The results show a significant improvement in forecast accuracy over unaided judgment, resulting in a subjective extrapolation that betters deseasonalized single exponential smoothing.
Some comparisons of the relative power of simple tests for structural change in regression models
Authors: Bleaney Michael
Published: July 2007

The power of Chow, linear, predictive failure and cusum of squares tests to detect structural change is compared in a two‐variable random walk model and a once‐for‐all parameter shift model. In each case the linear test has greatest power, followed by the Chow test. It is suggested that the linear test be used as the basic general test for structural change in time series data, and tests of forecasting performance be confined to the last few observations. Analysis of recursive residuals and recursive parameter estimates should be regarded as forms of exploratory data analysis and tools for understanding discrepancies with previous results rather than a basis for formal tests of structural change.
Exponential smoothing: Estimation by maximum likelihood
Authors: Broze Laurence , Mélard Guy
Published: July 2007

In this paper several forecasting methods based on exponential smoothing with an underlying seasonal autoregressive‐moving average (SARIMA) model are considered. The relations between the smoothing constants and the coefficients of the autoregressive and moving average polynomials are used. On that basis, a maximum likelihood procedure for parameter estimation is described. The approach rules out the need for initial smoothed values. Prediction intervals are also obtained as a by‐product of the approach and a fast algorithm for implementing the method is outlined.
Estimating trend growth rates of the U.K. monetary aggregates
Authors: Mills Terence C.
Published: July 2007

This paper constructs current trend growth rates for a variety of U.K. monetary aggregates. These rates are computed from decompositions of intervention‐augmented ARIMA models, the interventions being identified and their magnitude estimated by an iterative detection procedure.
Models for the assessment of the value of forecast information
Authors: Lawrence David B.
Published: July 2007

This paper reviews, extends and applies alternative normative decision models for the assessment of the value of forecast information, concentrating primarily on the factors influencing the tractability of assessment and interpretation in specific decision problems. As an empirical illustration of t lhe models, the paper presents a valuation analysis of the published judgmental price forecasts of a veteran analyst of the hog market to a perfectly competitive farm enterprise.
When and how to use scenario planning: A heuristic approach with illustration
Authors: Schoemaker Paul J. H.
Published: July 2007

This paper offers a step‐by‐step analysis of a heuristic approach to scenario planning, taking a managerial perspective. The scenario method is contrasted in general with more traditional planning techniques, which tend to perform less well when faced with high uncertainty and complexity. An actual case involving a manufacturing company is used to illustrate the main steps of the proposed heuristic. Its essence is to identify relevant trends and uncertainties, and blend them into scenarios that are internally consistent. In addition, the scenarios should bound the range of plausible uncertainties and challenge managerial thinking. Links to decision making are examined next, including administrative policies as well as integrative techniques. At the strategic level, a key‐success‐factor matrix is proposed for integrating scenarios, competitor analysis and strategic vision. At the operational level, Monte Carlo simulation is suggested and illustrated as one useful technique for combining scenario thinking with formal project evaluation (after appropriate translations). The paper concludes with a general discussion of scenario planning, to place it in a broader perspective.
Scoring the composite leading indicators: A Bayesian turning points approach
Authors: Lesage James P.
Published: July 2007

This paper explores a Bayesian decision‐theoretic approach for analysis and development of composite leading indicators. The methods used here are derived from work by Zellneret al.(1988) and Zellner and Hong (1988) aimed at forecasts time series turning points, and the multi‐process mixture models first described by Harrison and Stevens (1976) and more recently in West and Harrison (1989). Here, these methods are used to develop composite leading indicators formed by using the posterior probabilities derived from predictive relations between the individual indicator variables and the state of the economy measure as weights. This study, like those of Wecker (1979), Kling (1987) and Diebold and Rudebusch (1989), uses the time series observations on the measure of economic activity which we wish to predict along with an explicit definition of a turning point, either a downturn or upturn. Unlike those studies, we then establish a predictive relation between the individual component indicator series and the variable measuring economic activity which allows a Bayesian computation of probabilities associated with the turning point events. This parallels the developments in Zellneret al.(1988), where the focus was on forecasting turning points in economic times series. These probabilities are conditioned on the past data and the predictive probability density function (pdf) for future observations. A composite indicator is devised using a Class I, multi‐process mixture model suggested in West and Harrision (1989). The composite indicator arising from this approach is an average of the individual component series, where the averaging is done over the posterior probabilities of the individual component series predictive relations. An example of the procedure is provided using the national composite leading indicator (CLI).
Exponential smoothing: Behavior of theex‐postsum of squares near 0 and 1
Authors: Farnum Nicholas R.
Published: July 2007

When using simple exponential smoothing on a given time series the nature of the relationship between the optimal smoothing constant and the autocorrelation structure of the series remains an unresolved question. Although numerical search routines can easily be used to find optimal values of the smoothing constant, they offer little insight into the nature of the relationship between the estimated smoothing constant and the structure of the underlying time series. We suggest that renewed investigations of theex‐postsum of squares function may prove helpful in this pursuit. Results are presented that illustrate how the optimal smoothing constant depends upon the value used to initialize the smoothing and upon the sample autocorrelation coefficients of the observed series. These results are based on a new formula for the derivative of theex‐postsum of squares function. In particular, the derivative is examined near 0 and 1, where great simplifications occur in its form, thereby facilitating investigations near these points. A necessary and sufficient condition is stated for when theex‐postsum of squares must have a positive derivative at 0 and the autocorrelation coefficients of the differenced series are shown to affect the sign of the derivative near 1. Based on these results, a general algorithm is presented as an alternative to grid search routines for minimizing theex‐postsum of squares.
Time‐series forecasting of the German unemployment rate
Authors: Funke Michael
Published: July 2007

The purpose of the paper is to investigate the accuracy of forecasts derived from univariate and multivariate time‐series models. An iterative method to adjust for impact assessment in univariate ARIMA models is discussed and illustrated for the German unemployment rate. Finally, we also examine the pros and cons of the impact assessment model in comparison with VAR models.
Nonlinear predictor models
Authors: Ameen Jamal R. M.
Published: July 2007

A complete dynamic model is introduced within the Bayesian framework. This includes the dynamic linear model and the normal discount Bayesian model as special cases and extends to some well‐known models with nonlinear predictors. A number of practically important models are formulated and simple recurrence formulas, similar to those of Kalman, are used in the sequential estimation of the parameters. Finally, a number of practical examples and applications are given.
Hierarchical bayes forecasts of multinomial dirichlet data applied to coupon redemptions
Authors: Lenk Peter J.
Published: July 2007

This paper considers forecasting count data from a multinomial Dirichlet distribution. The forecasting procedure implements hierarchical Bayes methods in order to develop a prior distribution for a new series of data. The methodology is applied to the redemption of cents‐off promotional coupons. In a forecasting experiment, early forecasts of new series are similar to those from pooling all redemptions from previous coupon promotions. However, the hierarchical Bayes model provides realistic estimates of forecasting errors, while those for the pooled forecasts are consistently optimistic. As the current series evolves, the hierarchical Bayes forecasts adapt more rapidly and are more accurate than pooled forecasts.
To combine or not to combine? issues of combining forecasts
Authors: Palm Franz C. , Zellner Arnold
Published: July 2007

This paper addresses issues such as: Does it always pay to combine individual forecasts of a variable? Should one combine an unbiased forecast with one that is heavily biased? Should one use optimal weights as suggested by Bates and Granger over twenty years ago? A simple model which accounts for the main features of individual forecasts is put forward. Bayesian analysis of the model using noninformative and informative prior probability densities is provided which extends and generalizes results obtained by Winkler (1981) and compared with non‐Bayesian methods of combining forecasts relying explicitly on a statistical model for the individual forecasts. It is shown that in some instances it is sensible to use a simple average of individual forecasts instead of using Bates and Granger type weights. Finally, model uncertainty is considered and the issue of combining different models for individual forecasts is addressed.
Evaluating the role judgment plays in forecast accuracy
Authors: Donihue Michael R.
Published: July 2007

The majority of model‐based forecasting efforts today rely on relatively simple techniques of estimation and the subjective adjustment of the model's results to produce forecasts. Published forecasts reflect to a great extent the judgment of the forecaster rather than what the model by itself has to say about the future. This paper examines the role judgment plays in the process of producing a macroeconometric forecast. The debate over the use of adjustment constants to alter the statistical results of a model is outlined and an empirical analysis of forecasts generated by the Michigan Quarterly Econometric Model of the US economy is presented using a unique data set which isolates the role of judgment in the forecasting process.
The effectiveness of imprecise probability forecasts
Authors: George Benson P. , Whitcomb Kathleen M.
Published: July 2007

In this paper we investigate the feasibility of algorithmically deriving precise probability forecasts from imprecise forecasts. We provide an empirical evaluation of precise probabilities that have been derived from two types of imprecise probability forecasts: probability intervals and probability intervals with second‐order probability distributions. The minimum cross‐entropy (MCE) principle is applied to the former to derive precise (i.e. additive) probabilities; expectation (EX) is used to derive precise probabilities in the latter case. Probability intervals that were constructed without second‐order probabilities tended to be narrower than and contained in those that were amplified by second‐order probabilities. Evidence that this narrowness is due to motivational bias is presented. Analysis of forecasters' mean Probability Scores for the derived precise probabilities indicates that it is possible to derive precise forecasts whose external correspondence is as good as directly assessed precise probability forecasts. The forecasts of the EX method, however, are more like the directly assessed precise forecasts than those of the MCE method.
Fixed versus time‐varying transfer functions for modelling business cycles
Authors: Edlund Per‐Olov , T. Søgaards Henning
Published: July 2007

Transfer function models can be used to model the relationship between leading indicators and the business cycle. The traditional transfer function approach assumes that the pure delay (lead) as well as the other parameters of the model are constant. Results from studies on Swedish business cycle data indicate that the relationship between the leading indicators and the business cycle may be time varying. In this paper the traditional approach is compared to a recursive estimation procedure that allows the model to change over time. The results show that the recursive procedure is useful and that the estimates of the model parameters indeed vary over time.
Insights or forecasts? An evaluation of a computable general equilibrium model of spain
Authors: Polo Clemente , Sancho Ferran
Published: July 2007

Computable general equilibrium (CGE) models are widely used as an advanced tool to evaluate alternative economic strategies and policy measures. These models are well rooted in solid economic theory, yet a crucial question is hardly asked: how well do these models perform? We address this question by comparing the economic performance of the Spanish economy in 1988 with the simulation results drawn from a CGE model calibrated with a 1987 Social Accounting Matrix. The values of endogenous variables used in the comparison are the equilibrium values provided by the model after updating the values of exogenous variables such as labour and capital endowments, real exports and effective nominal exchange rates with the European Community and the rest of the world, real government expenditures, and various tax rates, government subsidies, and transfers. The comparison shows that the model captures adequately the major developments that occurred in the Spanish economy in 1988. This result increases our confidence in the quantitative estimates derived from the model in the usual simulation exercises.
On estimation and prediction procedures for ar(1) models with power transformation
Authors: Lee J. C. , Tsao S. L.
Published: July 2007

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.
Diagnostic tracking and model specification in combined forecasts of U.K. inflation
Authors: Menezes Lilian De , Bunn Derek W.
Published: July 2007

Combinations of several forecasts are now quite commonly used as inputs into business planning models. For example, capital budgeting generally involves a synthesis of several sources of economic forecasts. In such cases, where uncertainty and risk are also being explicitly considered, the statistical specification of the combined forecasts becomes particularly important. An investigation of the monthly forecasts of annual inflation from nine leading U.K. economic models was undertaken to examine the circumstances under which well‐specified and efficient combined forecasts could be produced. This has helped to refine the general guidelines for the practical use of combinations in planning models.
Bootstrapping forecast intervals: An application to AR(p) models
Authors: McCullough B. D.
Published: July 2007

Forecast intervals typically depend upon an assumption of normal forecast errors due to lack of information concerning the distribution of the forecast. This article applies the bootstrap to the problem of estimating forecast intervals for an AR(p) model. Box‐Jenkins intervals are compared to intervals produced from a naive bootstrap and a bias‐correction bootstrap. Substantial differences between the three methods are found. Bootstrapping an AR(p) model requires use of the backward residuals which typically are not i.i.d. and hence inappropriate for bootstrap resampling. A recently developed method of obtaining i.i.d. backward residuals is employed and was found to affect the bootstrap prediction intervals.
Time‐variable parameter and trend estimation in non‐stationary economic time series
Authors: Young Peter C.
Published: July 2007

The paper describes a general approach to the modelling of nonlinear and nonstationary economic systems from time‐series data. This method exploits recursive state space filtering and fixed interval smoothing algorithms to decompose the time‐series into long term trend and short term ‘small perturbational’ components, each of which are then modelled by linear stochastic models which may be characterised by time variable parameters. The approach is illustrated by an example which explores the relationship between the variations in quarterly GNP and Unemployment in the USA over the period 1948 to 1988 and questions previous claims about the changes in the slope of the long term trend in loge(GNP) over this same period. The paper also points out that the recursive approach to estimation facilitates the use of these methods in the development of adaptive forecasting and control systems.
Predicting cyclical turning points with leading index in a markov switching model
Authors: Lahiri K. , Wang J. G.
Published: July 2007

We have evaluated the Commerce Department's Composite Index of Leading Indicators as a predictor of business cycle turning points using the two‐state Markov switching model as the filter. Contrary to some recent studies, we found that the predictive performance of CLI is quite good and, with an exception of the 1973:11 peak, it made very little difference to the prediction of turning points whether real‐time data are used instead of the revised series. We found, however, that imposing any degree of autoregression in the errors on the simple regime‐shift model caused the filter to signal turning points inappropriately. Also, we found no evidence of duration dependence in post‐war U.S. business cycles.
Time‐series forecasting using fractional differencing
Authors: Sutcliffe Andrew
Published: July 2007

The main failure of ARIMA modelling as used in practice are the limiting constraints imposed by differencing to achieve stationarity. The use of fractional differencing opens up a much wider and realistic behaviour for the trend and seasonal components than traditional integer differencing. This paper shows several advantages of using fractional differencing for forecasting monthly data. These advantages are illustrated using a sample of previously modelled time‐series data selected from the literature.
Modeling and forecasting time series with a general non‐normal distribution
Authors: Swift A. Louise
Published: July 2007

We propose a model for time series with a general marginal distribution given by the Johnson family of distributions. We investigate for which Johnson distributions forecasting using the model is likely to be most effective compared to using a linear model. Monte Carlo simulation is used to assess the reliability of methods for determining which of the three Johnson forms is most appropriate for a given series. Finally, we give model fitting and forecasting results using the modeling procedure on a selection of simulated and real time series.
Testing cumulative prediction errors in event study methodology
Authors: Coutts J. Andrew , Mills Terence C. , Roberts Jennifer
Published: July 2007

This paper reconsiders event study methodology, a very popular technique in the applied finance literature, within the context of testing cumulative prediction errors. It extends the conventional test statistics in two directions. First, it accounts fully for the increased variance of prediction errors outside of the estimation period and for the cumulation of these errors across different event windows. Second, it also takes account of the fact that market model residuals are typically serially correlated, heteroscedastic and non‐normal. The statistics are compared with the conventional approach by reassessing a previous application of the methodology to the impact of management buyouts.
BVAR as a category management tool: An illustration and comparison with alternative techniques
Authors: Curry David J. , Divakar Suresh , Mathur Sharat K. , Whiteman Charles H.
Published: July 2007

Category management—a relatively new function in marketing—involves large‐scale, real‐time forecasting of multiple data series in complex environments. In this paper, we illustrate how Bayesian Vector Auto regression (BVAR) fulfils the category manager's decision‐support requirements by providing accurate forecasts of a category's state variables (prices, volumes and advertising levels), incorporating management interventions (merchandising events such as end‐aisle displays), and revealing competitive dynamics through impulse response analyses. Using 124 weeks of point‐of‐sale scanner data comprising 31 variables for four brands, we compare the out‐of‐sample forecasts from BVAR to forecasts from exponential smoothing, univariate and multivariate Box‐Jenkins transfer function analyses, and multivariate ARMA models. TheilU'sindicate that BVAR forecasts are superior to those from alternate approaches. In large‐scale forecasting applications, BVAR's ease of identification and parsimonious use of degrees of freedom are particularly valuable.
An integrated bayesian vector auto regression and error correction model for forecasting electricity consumption and prices
Authors: Joutz Frederick L , Maddala G. S. , Trost Robert P.
Published: July 2007

The analysis and forecasting of electricity consumption and prices has received considerable attention over the past forty years. In the 1950s and 1960s most of these forecasts and analyses were generated by simultaneous equation econometric models. Beginning in the 1970s, there was a shift in the modeling of economic variables from the structural equations approach with strong identifying restrictions towards a joint time‐series model with very few restrictions. One such model is the vector auto regression (VAR) model. It was soon discovered that the unrestricted VAR models do not forecast well. The Bayesian vector auto regression (BVAR) approach as well the error correction model (ECM) and models based on the theory of co integration have been offered as alternatives to the simple VAR model. This paper argues that the BVAF., ECM, and co integration models are simply VAR models with various restrictions placed on the coefficients. Based on this notion of a restricted VAR model, a four‐step procedure for specifying VAR forecasting models is presented and then applied to monthly data on US electricity consumption and prices.
1H and 13C NMR signal assignments of Paecilin A and B, two new chromone derivatives from mangrove endophytic fungus Paecilomyces sp. (tree 1–7)
Authors: Guo Zhiyong , She Zhigang , Shao Changlun , Wen Lu , Liu Fan , Zheng Zhonghui , Lin Yongcheng
Published: July 2007

Two new natural products, named paecilin A (1) and B (2), together with two known compounds secalonic acid D (3) and (11)‐cytochalasa‐6(12),13‐diene‐1,21‐dione‐16,18‐dimethyl‐7‐hydroxy‐l0‐phenyl‐(7S*,13E,16S*,18S*) (4), were isolated from the mangrove endophytic fungus, Paecilomyces sp. (tree 1–7) from the South China Sea. 1D and 2D NMR experiments including COSY, HMQC, and HMBC were used for the determination of their structures. In our cytotoxicity assays, secalonic D (3) showed cytotoxicity toward KB cells with IC50 < 1 µg ml−1 and inhibiting human topoisomerase I with IC50 at 0.16 µmol ml−1. 1, 2, and 4 showed no activity to KB cells. Copyright © 2007 John Wiley & Sons, Ltd.
Search operation methods in the relational database machine RINDA
Authors: Hayami Haruo , Satoh Tetsuji , Nakamura Toshio , Kuroiwa Junichi , Takeda Hideaki
Published: July 2007

Nonindexed queries in a relational database are performed by searching operations, i.e., read out all rows in a table from disks and repetitive condition judgment for each row. Therefore, it takes a long response time on software‐only systems.

This paper discusses a searching architecture implemented in a RINDA system which is an attached processor of relational database management systems (DBMS). Performance evaluation indicates that RINDA enhances the response of nonindexed queries by as much as 100 times compared to software‐only systems.

The proposed main features are as follows:

(1) The effective functions to achieve a short response time are selected and then implemented as a CSP hardware; (2) for a cooperative process with both CSP and software, the internal format of searching conditions is translated in conjunctive canonical form; (3) by executing NOT operator in a logical equation directly in predicates, the SQL three‐value logic degenerates into two‐value; and (4) by using multiple CSPs, parallel search processing is performed.
Proposal for an intermediary shape representation using dots on minimal rectangles
Authors: Yaginuma Yoshitomo , Sakauchi Masao
Published: July 2007

In handling the graphic data of a finite size, the minimal rectangle, which is the minimal rectangle surrounding the graphic data, or the minimal sphere, which is the minimal sphere surrounding the data, often is employed as the intermediary representation of the data to speed‐up the retrieval and manipulation of the graphic data. However, when most of the retrieval time is occupied by the check of the intersection rather than by the tracing of the data structure, as in the case of complex data or data placed on the external memory with a lower speed, a more efficient intermediary representationof the graphic data is required. This can reduce the number of intersection checks and speed‐up the retrieval, compared to the method based on the minimal rectangle or the minimal sphere.

From such a viewpoint, this paper proposes an intermediary representation of the graphic data using dots on the minimal rectangle as one such intermediary representation of the data. Theoretical as well as experimental evaluations are presented. It is seen as a result of the theoretical calculation and simulation experiment that compared to the method based on the minimal rectangle, the proposed method can reduce drastically the number of intersection checks which helps to reduce the actual retrieval time. In the case of straight‐line data, for example, of length less than the edge length of the retrieval area, the number of intersection checks can be reduced to one‐fourth that of the method based on the minimal rectangle, even in the worst case.
Sequential circuit test generation by real number simulation
Authors: Hikone Kazunori , Ikeda Mitsuji , Hatayama Kazumi , Hayashi Terumine
Published: July 2007

This paper presents a test generation method using an optimization technique for a single stuck‐at fault in synchronous sequential circuits. This method utilizes a new real number simulation for defining the cost of an input pattern for a given fault and leads an input pattern to a test pattern by changing the input repeatedly to minimize its cost.

Since a sequential circuit has internal states, a test pattern for a fault is a sequence of input vectors for several time frames. For generating the whole test pattern, the concept of forward test generation, which performs the convergence calculation process at multiple time frames, is introduced to avoid the problem of local optimum.

Experimental results for ISCAS '89 benchmark sequential circuits show the effectiveness of the proposed method for sequential circuit test generation.
Predictor codebook for speaker‐independent speech recognition
Authors: Kawabata Takeshi
Published: July 2007

This paper discusses a method to handle the diversified dynamic features of speech by representing the dynamic features of speech by spectrum predictors and constructing the codebook containing predictors as the elements. The effectiveness of the method for speaker‐independent speech recognition is examined. Three kinds of predictor structures, i.e., the forward predictor, the backward predictor and the interpolator, are examined. The predictor codebook is constructed by the predictor quantization procedure, which is a small modification of the LBG algorithm. For the evaluation of the phoneme recognition level, two kinds statistical evaluation quantities and the phoneme recognition rate have been considered. It is seen as a result that the predictor codebook can realize a high phoneme separation capability and the robustness against the speaker variation. By combining the process actually into the phrase recognition system, the performance at the continuous speech recognition level is evaluated. In either case, the codebook with the backward predictor as the elements exhibited the highest performance.
A randomized algorithm for finding a near‐maximum clique and its experimental evaluations
Authors: Yamada Yoshiaki , Tomita Etsuji , Takahashi Haruhisa
Published: July 2007

The so‐called Maximum Clique Problem is one of the most famous NP‐complete problems for which it is difficult to find a solution. Given an indirected graph, we present here a polynomial‐time randomized algorithm RaCLIQUE for finding a near‐maximum clique. While the basic idea of the algorithm comes from Boltzmann machines, it employs no simulated annealing at all and hence it is simple to control its execution. We have confirmed in experiments for several random and nonrandom graphs with up to 400 nodes that very good solutions can be found efficiently compared with the other conventional algorithms.
Motion Analysis of Nonrigid Objects by Active Contour Models Using Multiscale Images
Authors: Fujimura Kouta , Yokoya Naokazu , Yamamoto Kazuhiko
Published: July 2007

This paper considers the approach to dynamic image processing, which is one of the important problems in the future medical image processing. The tracking of the ojbect and the analysis of the motion are discussed for the dynamic images of a nonrigid object with smooth shape, motion and deformation, which is the case in most medical images. This approach is based on an active contour model defined by an energy function in terms of both intra‐ and interframe constraints for the contour of the object. The contour of the target object is extracted and tracked by minimizing the energy function using multiscale dynamic programming and the motion is analyzed.

The dynamic programming in multiscale proposed in this paper is to adjust the search neighborhood of the dynamic programming according to the scale. The coarse or fine neighborhood is defined for the coarse and fine scales, respectively, and the nergy is minimized starting from the coarse scale and shifting to the fine scale. By this scheme, a large motion an deformation of the object can be handled. The proposed motion tracking method has been applied successfully to the dynamic image in the “behavioral analysis of a slug aiming at the analysis of the neural mechanism of learning and memory formation in slugs,” as well as to dynamic echocardiographic images.

In the first application, the positive maximum of the curvature along the contour is extracted in the motion analysis as a characteristic point invariant to the deformation of the object. Then the shift of that point is traced. By this approach, the rough motion of the object can be estimated.
Scene interpretation based on boundary representations of stereo images
Authors: Sugimoto Kazuhide , Tomiya Fumiaki
Published: July 2007

Generally, extraction of boundary lines from a binocular stereo image is effective for a plane with patterns or a curved surface with shadows, and a place with uniform surface. Since a uniform plain cannot directly be reconstructed, however, another process to reconstruct the plain is needed after its boundary line is reconstructed. If a simple algorithm is applied to this process, a false plane is extracted sometimes. This paper describes a method uniquely to determine a plane from the three‐dimensional boundary lines of an object in the stereo image. The method is effective for any object consisting of planes in the image. The method can also locally detect a fault in the boundary line so that this makes its compensation process easy. The final output image of this method is available in a form of a ‘geometrical mode’.
Algorithms and performance evaluation of join processing on KD‐tree indexed relations
Authors: Kitsuregawa Masaru , Takagi Mikio , Harada Lilian
Published: July 2007

This paper proposes a new join processing algorithm for the KD‐tree indexed relations. The processing is evaluated by a simulation and the effectiveness is demonstrated. The KD‐tree index is one of the typical multidimensional clustering data structures. A flexible database can be constructed which provides uniform access characteristics in regard to the multiple attributes. On the other hand, the data structure is made complex, and the traditional join processing technique for the ordinary one‐dimensional clustering relation, which maintains the physical order, cannot be applied. Also, there has not been proposed a join technique for the KD‐tree indexed relations.

This paper introduces anew the concepts called “wave” and “join range,” and proposes an efficient join algorithm for the KD‐tree indexed relations. Then an extended algorithm is proposed by introducing the garbage collection function into the forementioned basic algorithm, where the unnecessary tuples on the main memory can be eliminated dynamically. Each of those algorithms is evaluated by a simulation, and the effectiveness is verified.

When the order of the join attribute is not preserved, the join processing by the hash‐based algorithm, which at present is considered the fastest, requires two read scannings and one write scanning, while the best algorithm proposed in this paper can execute the processing by almost one read scanning. Thus, a drastic improvement of the performance can be expected for the join processing by the proposed algorithm.
Priority management for concurrent execution of real‐time and on‐line transactions
Authors: Kataoka Ryoji , Satoh Tetsuji , Inoue Ushio
Published: July 2007

This paper presents priority management methods for concurrent execution of real‐time transactions with timing constraints and on‐line transactions without timing constraints in a real‐time database system (RTDBS). The aim of this study is the meeting of deadlines of real‐time transactions and shortening the response time of on‐line transactions at the same time. The proposed methods are Simple Mix, Biased Mix, and Balanced Mix, each of which differs in its strategy for assigning priorities to on‐line transactions. Simulation results show that Balanced Mix, which determines priorities dynamically according to the slack of real‐time transactions, can shorten the response time of on‐line transactions while keeping the deadline‐missing ratio of real‐time transactions low.
Estimation of the probability density function and aposterioriprobability by neural networks, and applications to vowel recognition
Authors: Nakagawa Seiichi , Ono Yoshiyuki
Published: July 2007

Feed‐forward neural networks have been used for pattern recognition, because they have an ability to estimatea posterioriprobability. This paper investigates the ability to estimate thea posterioriprobability by using one‐dimensional Gaussian distributions, uniform distributions, their mixed distributions and real speech data, and applies the networks to speech recognition. Furthermore, the ability to estimate a probability density function of artificial data by using a vector quantization technique and neural networks and also to apply them to speech recognition also are investigated. Feed‐forward neural networks, radial basis function networks (RBF), Gaussian mixed distributions and multitemplate methods for speech recognition are compared. It is concluded that the vector quantization‐based RBF is the best in practice.
Recovering 3D models from silhouette sequences and detecting unexposed regions
Authors: Zheng Jiang Yu , Kishino Fumio
Published: July 2007

This paper recovers 3D shapes from contours for graphics display in a 3D virtual environment. In this method, a continuous sequence of images is taken as an object rotates, and smooth convex shape is estimated instantaneously from its contour and contour movement. Shapes also are analyzed that do not satisfy the conditions of smoothness and visibility, which are indispensable for modeling an object. A region that is not exposed as contour yields a non‐smoothness in the tracked contour movement. An unknown region is obtained for further investigation using other visual cues. The entire process from silhouettes to a 3D model is based on local computation; which is promising for producing shapes in real time. The direct goal is to establish 3D graphics models of human faces for the growing needs of visual communications. Some good results have been obtained.
Sign language translation based on syntactic and semantic analysis
Authors: Abe Masahiro , Sakou Hiroshi , Sagawa Hirohiko
Published: July 2007

The prototype sign language translation system described herein interprets Japanese sign language to verbal Japanese language. Since sign language has a unique morphology and syntax, the sign language translation system requires linguistic translation after recognition of the input symbols. One of the main difficulties in translation is a result of the insufficiency of syntactic markers in the input information.

In Japanese sign language, input is represented as a sequence of important words. In our method, semantic dependency analysis based on case frame grammar is carried out to complement the lack of information such as postpositions. Then, rule‐based rephrasing is executed to obtain natural verbal language. This scheme contributes both flexibility and ease of maintenance to the translation system. A prototype sign language translation system was implemented and tested using 123 sentences extracted from a sign language textbook. Experimental results indicate that 76 percent of the sentences can be translated correctly if they have no recognition error in input sentence. However, if a recognition error occurs, the experiment indicates that the translation will become highly ambiguous. Therefore, the capability for rejecting incorrect sentences is very important, along with the capability for translating correct input sentences. Future issues include incorporation of unused information, such as facial expression, spatial information and contexts in sign language to improve translation accuracy.