0000000000027968
AUTHOR
José D. Bermúdez
Forecasting correlated time series with exponential smoothing models
Abstract This paper presents the Bayesian analysis of a general multivariate exponential smoothing model that allows us to forecast time series jointly, subject to correlated random disturbances. The general multivariate model, which can be formulated as a seemingly unrelated regression model, includes the previously studied homogeneous multivariate Holt-Winters’ model as a special case when all of the univariate series share a common structure. MCMC simulation techniques are required in order to approach the non-analytically tractable posterior distribution of the model parameters. The predictive distribution is then estimated using Monte Carlo integration. A Bayesian model selection crite…
A fuzzy ranking strategy for portfolio selection applied to the Spanish stock market
In this paper we present a fuzzy ranking procedure for the portfolio selection problem. The uncertainty on the returns of each portfolio is approximated by means of a trapezoidal fuzzy number. The expected return and risk of the portfolio are then characteristics of that fuzzy number. A rank index that accounts for both expected return and risk is defined, allowing the decision-maker to compare different portfolios. The paper ends with an application of that fuzzy ranking strategy to the Spanish stock market.
A multi-objective genetic algorithm for cardinality constrained fuzzy portfolio selection
This paper presents a new procedure that extends genetic algorithms from their traditional domain of optimization to fuzzy ranking strategy for selecting efficient portfolios of restricted cardinality. The uncertainty of the returns on a given portfolio is modeled using fuzzy quantities and a downside risk function is used to describe the investor's aversion to risk. The fitness functions are based both on the value and the ambiguity of the trapezoidal fuzzy number which represents the uncertainty on the return. The soft-computing approach allows us to consider uncertainty and vagueness in databases and also to incorporate subjective characteristics into the portfolio selection problem. We …
Bayesian forecasting with the Holt–Winters model
Exponential smoothing methods are widely used as forecasting techniques in inventory systems and business planning, where reliable prediction intervals are also required for a large number of series. This paper describes a Bayesian forecasting approach based on the Holt–Winters model, which allows obtaining accurate prediction intervals. We show how to build them incorporating the uncertainty due to the smoothing unknowns using a linear heteroscedastic model. That linear formulation simplifies obtaining the posterior distribution on the unknowns; a random sample from such posterior, which is not analytical, is provided using an acceptance sampling procedure and a Monte Carlo approach gives …
A Bayesian unified framework for risk estimation and cluster identification in small area health data analysis.
Many statistical models have been proposed to analyse small area disease data with the aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian hierarchical model that simultaneously allows for risk estimation and cluster identification. Our model formulation assumes that there is an unknown number of risk classes and small areas are assigned to a risk class by means of independent allocation variables. Therefore, areas within each cluster are assumed to share a common risk but they may be geographically separated. The posterior distribution of the parameter representing the number of risk classes is estimated using a novel procedure that combines its prior …
Fuzzy portfolio optimization under downside risk measures
This paper presents two fuzzy portfolio selection models where the objective is to minimize the downside risk constrained by a given expected return. We assume that the rates of returns on securities are approximated as LR-fuzzy numbers of the same shape, and that the expected return and risk are evaluated by interval-valued means. We establish the relationship between those mean-interval definitions for a given fuzzy portfolio by using suitable ordering relations. Finally, we formulate the portfolio selection problem as a linear program when the returns on the assets are of trapezoidal form.
SIOPRED performance in a Forecasting Blind Competition
In this paper we present the results obtained by applying our automatic forecasting support system, named SIOPRED, over a data set of time series in a Forecasting Blind Competition. In order to apply our procedure for providing point forecasts it has been necessary to develop an interactive strategy for the choice of the suitable length of the seasonal cycle and the seasonality form for a generalized exponential smoothing method, which have been obtained using SIOPRED. For the choice of those essential characteristics of forecasting methods, also a certain multi-objective formulation which minimizes several measures of fitting is used. Once these specifications are established, the model pa…
A Forecasting Support System Based on Exponential Smoothing
This chapter presents a forecasting support system based on the exponential smoothing scheme to forecast time-series data. Exponential smoothing methods are simple to apply, which facilitates computation and considerably reduces data storage requirements. Consequently, they are widely used as forecasting techniques in inventory systems and business planning. After selecting the most adequate model to replicate patterns of the time series under study, the system provides accurate forecasts which can play decisive roles in organizational planning, budgeting and performance monitoring.
Angiogenesis in neuroblastoma: relationship to survival and other prognostic factors in a cohort of neuroblastoma patients.
PURPOSE: To study angiogenesis in neuroblastoma, using morphometric and computerized image analysis, and correlate the results with survival and other prognostic factors. PATIENTS AND METHODS: Sixty-nine patients from the Spanish Cooperative Study for Neuroblastoma were studied. Tumoral angiogenesis was studied using an avidin-biotin immunoperoxidase technique with an anti-CD34 antibody. Vascular parameters (VPs) were analyzed by a computerized system. Statistical analysis was also performed. RESULTS: Sixty-six samples had adequate tumoral tissue, and their tumoral vessels were counted. Endothelial cells were more prominent in pure neuroblastomas than in maturing and more mature tumors. VP…
Holt–Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data
Abstract This paper provides a formulation for the additive Holt–Winters forecasting procedure that simplifies both obtaining maximum likelihood estimates of all unknowns, smoothing parameters and initial conditions, and the computation of point forecasts and reliable predictive intervals. The stochastic component of the model is introduced by means of additive, uncorrelated, homoscedastic and Normal errors, and then the joint distribution of the data vector, a multivariate Normal distribution, is obtained. In the case where a data transformation was used to improve the fit of the model, cumulative forecasts are obtained here using a Monte-Carlo approximation. This paper describes the metho…
Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection
Graphical abstractDisplay Omitted HighlightsWe consider a constrained three-objective optimization portfolio selection problem.We solve the problem by means of evolutionary multi-objective optimization.New mutation, crossover and reparation operators are designed for this problem.They are tested in several algorithms for a data set from the Spanish stock market.Results for two performance metrics reveal the effectiveness of the new operators. In this paper, we consider a recently proposed model for portfolio selection, called Mean-Downside Risk-Skewness (MDRS) model. This modelling approach takes into account both the multidimensional nature of the portfolio selection problem and the requir…
Effect of a surveillance system for decreasing neonatal nosocomial infections.
Nosocomial infection in very low birthweight (VLBW) infants is a common complication with high morbimortality. New strategies to reduce its occurrence have recently led to the development of neonatal surveillance programs.To determine whether the NeoKissEs surveillance system implementation in our neonatal unit has been associated with a decrease in nosocomial infection in VLBW infants, as well as a reduction in the use of antibiotics and central venous catheters (CVC).Retrospective and descriptive study of infants1500 g admitted between January 2011 and December 2017. Rates of use of antibiotics and CVC were calculated, as well as late-onset sepsis incidence. Data were compared before and …
Eleccion de variables en regresion lineal un problema de decision
A general structure for the problem of selection of variables in regression is proposed using the decision theory framework. In particular, some results for the choice of the best linear normal homocedastic model are obtained when the main purpose is either to specify the predictive distribution over the response variable or to obtain a point estimate of it. A comparison of our results with the most widespread classical ones is presented
Forecasting time series with missing data using Holt's model
This paper deals with the prediction of time series with missing data using an alternative formulation for Holt's model with additive errors. This formulation simplifies both the calculus of maximum likelihood estimators of all the unknowns in the model and the calculus of point forecasts. In the presence of missing data, the EM algorithm is used to obtain maximum likelihood estimates and point forecasts. Based on this application we propose a leave-one-out algorithm for the data transformation selection problem which allows us to analyse Holt's model with multiplicative errors. Some numerical results show the performance of these procedures for obtaining robust forecasts.
Hypermethylation of apoptotic genes as independent prognostic factor in neuroblastoma disease
Neuroblastoma (NB) is an embryonal tumour of neuroectodermal cells, and its prognosis is based on patient age at diagnosis, tumour stage and MYCN amplification, but it can also be classified according to their degree of methylation. Considering that epigenetic aberrations could influence patient survival, we studied the methylation status of a series of 17 genes functionally involved in different cellular pathways in patients with NB and their impact on survival. We studied 82 primary NB tumours and we used methylation-specific-PCR to perform the epigenetic analysis. We evaluated the putative association among the evidence of hypermethylation with the most important NB prognostic factors, a…
MLML2R: an R package for maximum likelihood estimation of DNA methylation and hydroxymethylation proportions.
Abstract Accurately measuring epigenetic marks such as 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) at the single-nucleotide level, requires combining data from DNA processing methods including traditional (BS), oxidative (oxBS) or Tet-Assisted (TAB) bisulfite conversion. We introduce the R package MLML2R, which provides maximum likelihood estimates (MLE) of 5-mC and 5-hmC proportions. While all other available R packages provide 5-mC and 5-hmC MLEs only for the oxBS+BS combination, MLML2R also provides MLE for TAB combinations. For combinations of any two of the methods, we derived the pool-adjacent-violators algorithm (PAVA) exact constrained MLE in analytical form. For the…
Arsenic exposure, diabetes-related genes and diabetes prevalence in a general population from Spain.
Inorganic arsenic exposure may be associated with diabetes, but the evidence at low-moderate levels is not sufficient. Polymorphisms in diabetes-related genes have been involved in diabetes risk. We evaluated the association of inorganic arsenic exposure on diabetes in the Hortega Study, a representative sample of a general population from Valladolid, Spain. Total urine arsenic was measured in 1,451 adults. Urine arsenic speciation was available in 295 randomly selected participants. To account for the confounding introduced by non-toxic seafood arsenicals, we designed a multiple imputation model to predict the missing arsenobetaine levels. The prevalence of diabetes was 8.3%. The geometric…
Sterols in human milk during lactation: bioaccessibility and estimated intakes.
Human milk (HM) is the exclusive food during the first 4–6 months of an infant's life. Breastfeeding has been related to significant health benefits for infants, and hence it is of interest to study the bioactive compounds present in HM, such as sterols (cholesterol being the most abundant). The aim of this study was to determine the contents of sterols (cholesterol, desmosterol, lathosterol, lanosterol, campesterol, stigmasterol and β-sitosterol) in 10 pools of colostrum, transitional milk, and 1, 3 and 6 month HM obtained from Spanish volunteers from two different geographical areas (coastal and central) and to estimate the intake and bioaccessibility (BA) of sterols in order to ascertain…
Introducing a Fuzzy-Pattern Operator in Fuzzy Time Series
In this paper we introduce a fuzzy pattern operator and propose a new weighting fuzzy time series strategy for generating accurate ex-post forecasts. A decision support system is built for managing the weights of the information provided by the historical data, under a fuzzy time series framework. Our procedure analyzes the historical performance of the time series using different experiments, and it classifies the characteristics of the series through a fuzzy operator, providing a trapezoidal fuzzy number as one-step ahead forecast. We also present some numerical results related to the predictive performance of our procedure with time series of financial data sets.
A statistical study of the quality of surface water intended for human consumption near Valencia (Spain)
Water quality in the European Union is subject to legislation through directives that are applicable in all Member States. The directives specify a set of physical and chemical parameters that should be regularly controlled using a network of sampling points, with sampling based on the intended use of the water. This paper presents the results of a statistical comparison of the quality of water intended for human consumption at two different locations (the Canal de Benagéber and the Canal Júcar-Turia) near the town of Valencia (Spain). These are currently the only canals that could supply Valencia and other nearby towns with drinking water. The parameters considered in this paper are the on…
Multivariate exponential smoothing: A Bayesian forecast approach based on simulation
This paper deals with the prediction of time series with correlated errors at each time point using a Bayesian forecast approach based on the multivariate Holt-Winters model. Assuming that each of the univariate time series comes from the univariate Holt-Winters model, all of them sharing a common structure, the multivariate Holt-Winters model can be formulated as a traditional multivariate regression model. This formulation facilitates obtaining the posterior distribution of the model parameters, which is not analytically tractable: simulation is needed. An acceptance sampling procedure is used in order to obtain a sample from this posterior distribution. Using Monte Carlo integration the …
Influence of nutritional variables on the onset of necrotizing enterocolitis in preterm infants: A case-control study.
• Minimal enteral feeding should be early initiated and prolonged for at least 5–7 days in the most immature newborn.
Fuzzy Portfolio Selection Models: A Numerical Study
In this chapter we analyze the numerical performance of some possibilistic models for selecting portfolios in the framework of risk-return trade-off. Portfolio optimization deals with the problem of how to allocate wealth among several assets, taking into account the uncertainty involved in the behavior of the financial markets. Different approaches for quantifying the uncertainty of the future return on the investment are considered: either assuming that the return on every individual asset is modeled as a fuzzy number or directly measuring the uncertainty associated with the return on a given portfolio. Conflicting goals representing the uncertain return on and risk of a fuzzy portfolio a…
A fuzzy decision support tool for demand forecasting
In this paper we present a decision support forecasting system to work with univariate time series based on the generalized exponential smoothing (Holt-Winters) approach. It is conceived as an integrated tool which has been implemented in Visual Basic. For improving the accuracy of the automatic forecasting it uses an optimization-based scheme which unifies the stages of estimation of the parameters and selects the best method using a fuzzy multicriteria approach. The elements of the set of local minima of the non-linear programming problems allow us to build the membership functions of the conflicting objectives. A set of real data is analyzed to show the performance of our forecasting too…
Optimizing the level of service quality of a bike-sharing system
Public bike-sharing programs have been deployed in hundreds of cities worldwide, improving mobility in a socially equitable and environmentally sustainable way. However, the quality of the service is drastically affected by imbalances in the distribution of bicycles among stations. We address this problem in two stages. First, we estimate the unsatisfied demand (lack of free lockers or lack of bicycles) at each station for a given time period in the future and for each possible number of bicycles at the beginning of the period. In a second stage, we use these estimates to guide our redistribution algorithms. Computational results using real data from the bike-sharing system in Palma de Mall…
A decision support system methodology for forecasting of time series based on soft computing
Exponential procedures are widely used as forecasting techniques for inventory control and business planning. A number of modifications to the generalized exponential smoothing (Holt-Winters) approach to forecasting univariate time series is presented, which have been adapted into a tool for decision support systems. This methodology unifies the phases of estimation and model selection into just one optimization framework which permits the identification of robust solutions. This procedure may provide forecasts from different versions of exponential smoothing by fitting the updated formulas of Holt-Winters and selects the best method using a fuzzy multicriteria approach. The elements of the…
Portfolio optimization using a credibility mean-absolute semi-deviation model
We present a cardinality constrained credibility mean-absolute semi-deviation model.We prove relationships for possibility and credibility moments for LR-fuzzy variables.The return on a given portfolio is modeled by means of LR-type fuzzy variables.We solve the portfolio selection problem using an evolutionary procedure with a DSS.We select best portfolio from Pareto-front with a ranking strategy based on Fuzzy VaR. We introduce a cardinality constrained multi-objective optimization problem for generating efficient portfolios within a fuzzy mean-absolute deviation framework. We assume that the return on a given portfolio is modeled by means of LR-type fuzzy variables, whose credibility dist…
Measuring Uncertainty in the Portfolio Selection Problem
In this paper, we propose a new index for ranking portfolios based on the credibility expected return and loss on their investment. We assume that the return on a given portfolio is modeled as a trapezoidal fuzzy variable, whose credibility distribution is built using the data set of its historical returns. The credibilistic loss on the investment for a given portfolio is measured by means of a suitable loss function. In order to take risk-adverse investor attitudes into account, we analyze the performance of some credibility measures related to loss and risk on the investment for a given portfolio and their relationship with similar possibility measures. A numerical example is presented sh…
Improving demand forecasting accuracy using nonlinear programming software
We address the problem of forecasting real time series with a proportion of zero values and a great variability among the nonzero values. In order to calculate forecasts for a time series, the model coefficients must be estimated. The appropriate choice of values for the smoothing parameters in exponential smoothing methods relies on the minimization of the fitting errors of historical data. We adapt the generalized Holt–Winters formulation so that it can consider the starting values of the local components of level, trend and seasonality as decision variables of the nonlinear programming problem associated with this forecasting procedure. A spreadsheet model is used to solve the problems o…
Forecasting portfolio returns using weighted fuzzy time series methods
We propose using weighted fuzzy time series (FTS) methods to forecast the future performance of returns on portfolios. We model the uncertain parameters of the fuzzy portfolio selection models using a possibilistic interval-valued mean approach, and approximate the uncertain future return on a given portfolio by means of a trapezoidal fuzzy number. Introducing some modifications into the classical models of fuzzy time series, based on weighted operators, enables us to generate trapezoidal numbers as forecasts of the future performance of the portfolio returns. This fuzzy forecast makes it possible to approximate both the expected return and the risk of the investment through the value and a…
Exponential smoothing with covariates applied to electricity demand forecast
Exponential smoothing methods are widely used as forecasting techniques in industry and business. Their usual formulation, however, does not allow covariates to be used for introducing extra information into the forecasting process. In this paper, we analyse an extension of the exponential smoothing formulation that allows the use of covariates and the joint estimation of all the unknowns in the model, which improves the forecasting results. The whole procedure is detailed with a real example on forecasting the daily demand for electricity in Spain. The time series of daily electricity demand contains two seasonal patterns: here the within-week seasonal cycle is modelled as usual in exponen…
A new approach to portfolio selection based on forecasting
In this paper we analyze the portfolio selection problem from a novel perspective based on the analysis and prediction of the time series corresponding to the portfolio’s value. Namely, we define the value of a particular portfolio at the time of its acquisition. Using the time series of historical prices of the different financial assets, we calculate backward the value that said portfolio would have had in past time periods. A damped trend model is then used to analyze this time series and to predict the future values of the portfolio, providing estimates of the mean and variance for different forecasting horizons. These measures are used to formulate the portfolio selection problem, whic…
Síntomas, signos y estadística: Aplicaciones de la estadística en ciencias de la salud y de la vida
La determinación o constatación experimental de los mecanismos fisiológicos de las enfermedades suele ser una tarea muy compleja. Esto ha convertido a la epidemiología en la principal herramienta de generación de conocimiento en el ámbito médico. La epidemiología aprende sobre las enfermedades a partir de la observación de la salud en colectivos de personas, en lugar de la observación individual de éstas. Si la principal herramienta de generación de conocimiento médico se basa en la observación de colectivos de personas (muestras de una población) de las que querremos aprender (hacer inferencia), resulta claro el nexo entre la estadística y la medicina. A lo largo de este artículo ilustramo…
Impact of colonic fermentation on sterols after the intake of a plant sterol-enriched beverage: A randomized, double-blind crossover trial
Summary Background Cholesterol microbial transformation has been widely studied using in vitro fermentation assays, but less information is available on the biotransformation of plant sterols (PS). The excretion percentage of animal sterols (AS) (67–73%) is considerably greater than that of PS (27–33%) in feces from healthy humans following a Western diet. However, a lower content of AS in feces from subjects following a vegetarian, vegan or low-fat animal diet has been seen when compared to omnivorous subjects. Although only one human study has reported fecal sterol excretion after the consumption of PS-enriched food (8.6 g PS/day), it was found that the target group showed an increase in …
Exploring regression structure with graphics
We investigate the extent to which it may be possible to carry out a regression analysis using graphics alone, an idea that we refer to asgraphical regression. The limitations of this idea are explored. It is shown that graphical regression is theoretically possible with essentially no constraints on the conditional distribution of the response given the predictors, but with some conditions on marginal distribution of the predictors. Dimension reduction subspaces and added variable plots play a central role in the development. The possibility of useful methodology is explored through two examples.
Bayesian forecasting of demand time-series data with zero values
This paper describes the development of a Bayesian procedure to analyse and forecast positive demand time-series data with a proportion of zero values and a high level of variability for the non-zero data. The resulting forecasts play decisive roles in organisational planning, budgeting, and performance monitoring. Exponential smoothing methods are widely used as forecasting techniques in industry and business. However, they can be unsuitable for the analysis of non-negative demand time-series data with the aforementioned features. In this paper, an unconstrained latent demand underlying the observed demand is introduced into the linear heteroscedastic model associated with the Holt-Winters…