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41
Flexible least squares for temporal data mining and statistical arbitrage
 EXPERT SYSTEMS WITH APPLICATIONS
, 2009
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Technology forecasting for wireless communication
 Technovation
, 2008
"... Wireless communications technologies have undergone rapid changes over the last thirty years from analog approaches to digitalbased systems. These technologies have improved on many fronts including bandwidth, range, and power requirements. Development of new telecommunications technologies is crit ..."
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Cited by 8 (6 self)
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Wireless communications technologies have undergone rapid changes over the last thirty years from analog approaches to digitalbased systems. These technologies have improved on many fronts including bandwidth, range, and power requirements. Development of new telecommunications technologies is critical. It requires many years of efforts. In order to be competitive, it is critical to establish a roadmap of future technologies. This paper presents a framework to characterize, assess and forecast the wireless communication technologies. A DEAbased methodology was used for predicting the state of the art in future wireless communications technologies. Literature Review There are many techniques that can be used to develop technology forecasts. Linstone (1999) provides an overview of methods evolving over time. Other researchers
ConnexionistSystemsBased Long Term Prediction Approaches for Prognostics
, 2012
"... Abstract—Prognostics and Health Management aims at estimating the remaining useful life of a system (RUL), i.e. the remaining time before a failure occurs. It benefits thereby from an increasing interest: prognostic estimates (and related decisionmaking processes) enable increasing availability and ..."
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Abstract—Prognostics and Health Management aims at estimating the remaining useful life of a system (RUL), i.e. the remaining time before a failure occurs. It benefits thereby from an increasing interest: prognostic estimates (and related decisionmaking processes) enable increasing availability and safety of industrial equipment while reducing costs. However, prognostics is generally based on a prediction step which, in the context of datadriven approaches as considered in this paper, can be hard to achieve because future outcomes are in essence difficult to estimate. Also, a prognostic system must perform sufficient long term estimates, whereas many works focus on short term predictions. Following that, the aim of this paper is to formalize and discuss the connexionistsystemsbased approaches to ensure multistep ahead predictions for prognostics. Five approaches are pointed out: the Iterative, Direct, DirRec, Parallel, and MISMO approaches. Conclusions of the paper are based, on one side, on a literature review; and on the other side, on simulations among 111 time series prediction problems, and among a real engine fault prognostics application. These experiments are performed using the exTS (evolving extended TakagiSugeno system). As for comparison purpose, three types of performances measures are used: prediction accuracy, complexity (computational time), and implementation requirements. Results show that all three criteria are never optimized at the same time (same experiment), and best practices for prognostics application are finally pointed out. Index Terms—Prognostics and health management, multistep ahead predictions, connexionist system, evolving extended TakagiSugeno system. CBM exTS pdf RLS
DATADRIVEN MODEL EVALUATION: A TEST FOR REVEALED PERFORMANCE
, 2009
"... When comparing two competing approximate models, the one having smallest ‘expected true error’ is closest to the data generating process (according to the specified loss function) and is therefore to be preferred. In this paper we consider a datadriven method of testing whether two competing appr ..."
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When comparing two competing approximate models, the one having smallest ‘expected true error’ is closest to the data generating process (according to the specified loss function) and is therefore to be preferred. In this paper we consider a datadriven method of testing whether two competing approximate models, for instance a parametric and a nonparametric model, are equivalent in terms of their expected true error (i.e., their expected performance on unseen data drawn from the same data generating process). The proposed test is quite flexible with regards to the types of models and data types that can be compared (i.e., timeseries, cross section, panel etc.). Moreover, by applying our method to timeseries models we can overcome two of the drawbacks associated with existing approaches, namely, the reliance on only one split of the data and the need to have a sufficiently large holdout sample in order for the test to have power. Some useful graphical summaries are also presented. Finitesample performance and several illustrative applications are considered.
Multivariate Stochastic Volatility with Bayesian Dynamic Linear Models
, 2008
"... This paper develops a Bayesian procedure for estimation and forecasting of the volatility of multivariate time series. The foundation of this work is the matrixvariate dynamic linear model, for the volatility of which we adopt a multiplicative stochastic evolution, using Wishart and singular multiv ..."
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Cited by 4 (1 self)
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This paper develops a Bayesian procedure for estimation and forecasting of the volatility of multivariate time series. The foundation of this work is the matrixvariate dynamic linear model, for the volatility of which we adopt a multiplicative stochastic evolution, using Wishart and singular multivariate beta distributions. A diagonal matrix of discount factors is employed in order to discount the variances element by element and therefore allowing a flexible and pragmatic variance modelling approach. Diagnostic tests and sequential model monitoring are discussed in some detail. The proposed estimation theory is applied to a fourdimensional time series, comprising spot prices of aluminium, copper, lead and zinc of the London metal exchange. The empirical findings suggest that the proposed Bayesian procedure can be effectively applied to financial data, overcoming many of the disadvantages of existing volatility models.
Adaptive NeuroFuzzy Inference System for mid term prognostic error stabilization
, 2009
"... The high costs in maintaining complex equipments make necessary to enhance maintenance support systems and industrial and research communities take a growing interest in the prognostic process. However, this activity is still not well bounded and real prognostic systems are scarce. Thus, the general ..."
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The high costs in maintaining complex equipments make necessary to enhance maintenance support systems and industrial and research communities take a growing interest in the prognostic process. However, this activity is still not well bounded and real prognostic systems are scarce. Thus, the general purpose of the paper is to explore the way of performing failure prognostics so that manager can act consequently. The prognostic process is discussed from di erent points of view (concept, metrics, approaches and tools) in order to point out the pragmatic challenges of this activity. Assuming that maintenance decisions follow from a prediction step, the stabilization of mid term prediction errors appears to be essential. For that purpose a neurofuzzy predictor based on the ANFIS model is proposed to perform prognostic.
A Hybrid Approach for Modeling Financial Time Series
, 2010
"... Abstract: The problem we tackle concerns forecasting time series in financial markets. AutoRegressive MovingAverage (ARMA) methods and computational intelligence have also been used to tackle this problem. We propose a novel method for time series forecasting based on a hybrid combination of ARMA a ..."
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Abstract: The problem we tackle concerns forecasting time series in financial markets. AutoRegressive MovingAverage (ARMA) methods and computational intelligence have also been used to tackle this problem. We propose a novel method for time series forecasting based on a hybrid combination of ARMA and Gene Expression Programming (GEP) induced models. Time series from financial domains often encapsulate different linear and nonlinear patterns. ARMA models, although flexible, assume a linear form for the models. GEP evolves models adapting to the data without any restrictions with respect to the form of the model or its coefficients. Our approach benefits from the capability of ARMA to identify linear trends as well as GEP’s ability to obtain models that capture nonlinear patterns from data. Investigations are performed on real data sets. They show a definite improvement in the accuracy of forecasts of the hybrid method over pure ARMA and GEP used separately. Experimental results are analyzed and discussed. Conclusions and some directions for further research end the paper.
GBP/USD Currency Exchange Rate Time Series Forecasting Using Regularized LeastSquares Regression Method
, 2007
"... (RLSR)is a technique originally from Statistical Learning (SL) theory. RLSR can deal with nonlinear problem through mapping the samples into a higher dimension space using a kernel function. This paper adopts the RLSR to time series forecasting and the resulted model is termed RLSTS model getting ..."
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(RLSR)is a technique originally from Statistical Learning (SL) theory. RLSR can deal with nonlinear problem through mapping the samples into a higher dimension space using a kernel function. This paper adopts the RLSR to time series forecasting and the resulted model is termed RLSTS model getting the idea from applying neural network and support vector regression to time series forecasting. This paper applies the RLSTS model to GBP/USD Exchange Rate forecasting. RLSTS performs better than random walk, linear regression, autoregression integrated moving average, and artificial neural network model in predicting GBP/USD currency exchange rates. A grid search is used to choose the optimal parameters. Index Terms—exchange rate, regularized leastsquares, time series, forecasting.
Hybrid methodology for hourly global radiation forecasting in Mediterranean area
"... The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and AutoRegressive and Movin ..."
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The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and AutoRegressive and Moving Average (ARMA) model. While ANN by its nonlinear nature is effective to predict cloudy days, ARMA techniques are more dedicated to sunny days without cloud occurrences. Thus, three hybrids models are suggested: the first proposes simply to use ARMA for 6 months in spring and summer and to use an optimized ANN for the other part of the year; the second model is equivalent to the first but with a seasonal learning; the last model depends on the error occurred the previous hour. These models were used to forecast the hourly global radiation for five places in Mediterranean area. The forecasting performance was compared among several models: the 3 above mentioned models, the best ANN and ARMA for each location. In the best configuration, the coupling of ANN and ARMA allows an improvement of more than 1%, with a maximum in autumn (3.4%) and a minimum in winter (0.9%) where ANN alone is the best.
FORECASTING TIME SERIES WITH BOOT.EXPOS PROCEDURE
"... • To forecast future values of a time series is one of the main goals in times series analysis. Many forecasting methods have been developed and its performance evaluated. In order to make a selection among an avalanche of such emerging methods they have to be compared in a kind of forecasting comp ..."
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• To forecast future values of a time series is one of the main goals in times series analysis. Many forecasting methods have been developed and its performance evaluated. In order to make a selection among an avalanche of such emerging methods they have to be compared in a kind of forecasting competition. One of these competitions is the M3 competition with its 3003 time series. The competition results in Makridakis and Hibon (2000) paper are frequently used as a benchmark in comparative studies. The Boot.EXPOS approach developed by the authors, combines the use of exponential smoothing methods with the bootstrap methodology to forecast time series. The idea is to join these two approaches (bootstrap and exponential smoothing) and to construct a computational algorithm to obtain forecasts. It works in an automatic way and can be summarized as follows: (i) choose an exponential smoothing model, among several proposed using the mean squared error, and obtain the model components; (ii) fit an AR to the residuals of the adjusted model; the order of the AR is selected by AIC criterion; (iii) center the new residuals obtained in previous step and resample; (iv) obtain a bootstrapped replica of the time series according to the AR model and exponential smoothing components found in first step; (v) forecast future values according to model in (i); (vi) compute the point forecast as the mean or as the median of the predicted values. The performance of the procedure here proposed is evaluated by comparing it with other procedures presented in the M3 competition. Some accuracy measures are used for that comparison. All computational work is done using the R2.8.1 software (R Development Core Team, 2008). KeyWords: