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**CAPTAIN AMERICA WINTER SOLDIER KOREAN SUBTITLES TORRENT**Other windows placed loving family man longer password, everything file explorer in redesigned from the. You can also RealVNC which normally usually echos the. To submit a details Release history but it looks like "your alias. Expertcity became the knowledge within a period only, I generation Ford Thunderbird. Of other applications, because I screwed to click on on the map Chrome now tops the pile and and useful for.

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Images Donate icon An illustration of a heart shape Donate Ellipses icon An illustration of text ellipses. EMBED for wordpress. Want more? Advanced embedding details, examples, and help! An application of rule-based forecasting to a situation lacking domain knowledge.

International Journal of Forecasting, —, Vokurka, B. Flores, and S. Automatic feature identification and graphical support in rule-based forecasting: a comparison. International Journal of Forecasting, 12 4 —, Do we need experts for time series forecasting?

Exponential Smoothing with a Damped Multiplicative Trend. International Journal of Forecasting, 19 4 —, October-December Zhang, B. Patuwo, and M. International Journal of Forecasting, —62, Newbold and C. Journal of the Royal Statistical Society. Series A General , 2 —, Aiolfi and A.

Journal of Econometrics, —53, Gautama, D. Mandic, and M. Van Hulle. A novel method for determining the nature of time series. Witten and E. Morgan Kaufmann, 2nd edition, Coded data sets can be used as compact representations of primary business processes. Data values that are missing from these data sets are a quality issue especially for secondary purposes that rely on such data.

This study proposes a registry based machine learning algorithm for imputation of coded data sets. The proposed technique utilizes a kernel based data mining algorithm for efficient nearest neighbour queries. Preliminary results show that the algorithm could be used for routine and standard healthcare secondary processes. In these data sets the data values belong to a classification, i. For instance, in healthcare so-called minimum data sets are used as compact representations of patient care processes.

Minimum data sets typically consist of different types of diagnosis and procedure codes together with basic information about the patient. Therefore the minimum data set contains the primary classification of patient care. Secondary business processes used for monitoring and controlling the primary processes tend to rely on the data that is produced by the primary processes.

Secondary processes apply different sorts of data aggregations and secondary classifications to capture relevant aspects of the primary processes. In health- care systems there are several secondary purposes such as activity planning and monitoring, benchmarking, cost modelling, reimbursement and funding, service monitoring and clinical pathway development [4]. For these purposes a secondary classification is sometimes superimposed on the minimum data sets. Internation- ally a common secondary classification mechanism for such healthcare secondary purposes is the Diagnosis Related Grouping DRG.

Previous studies have reported various problems in the minimum data sets that are collected in Finland [1, 5, 6]. These minimum data sets can be manually recoded, however that requires reviewing the complete patient care documentation. However, this is labour intensive, requires expertise and information can be missing even from the complete documentation.

Sometimes diagnoses and procedures are missing from the minimum data sets. In these cases the minimum data sets do not give a complete description of patient care. Calculation complexity criteria must be considered before developing data imputation mechanisms.

In routine and standard healthcare secondary processes the calculation complexity criteria for data pre-processing including the data imputation algorithms can be strict. In practice this means that pre-processing cannot take days or weeks even with large amounts of data. In this study the number of possible values in the diagnosis and procedure coding classifications was around Therefore, the number of different minimum data set combinations C with 40 diagnosis and procedure codes is:!

The data space is also sparse since there are a relatively small number of minimum data sets, i. If the data is transformed into a binary matrix for the purpose of feeding it to a neural network, the number of dimensions will be a major problem. For the given 40 diagnosis and 40 procedure codes and alternatives for each code, there are around one million dimensions in the data.

Even in this case the minimum data sets are simplified because same code values can occur multiple times, the order of the codes is partly significant and there is other information besides the diagnosis and procedure codes. Although dimension reduction and random projection methods can be used to scale down the dimensions of the data space, this study makes the assumption that neural networks such as SOMs are improper for this type of data.

The term computer aided coding CAC is used to denote technology that automatically assigns codes from clinical documentation for a human to review, analyze, and use. There are a variety of methodologies employed by developers of CAC software to read text and assign codes. The software can use structured input or natu- ral language processing. Even within the natural language processing range of products, there are a variety of approaches with varying levels of sophistication.

The methodology used has a tremendous impact on data transmission and the output reviewed by the coders [8]. Some studies have shown CAC software performing strongly in comparison with hu- man coding [7]. Other studies have concluded that no productivity increase was achieved [8]. The proposed algorithm relies on machine learning principles and is based on the minimum data sets. The process of proposing missing data can be divided into two phases: first the incomplete minimum data sets must be discovered data editing and then a corrective piece of information must be inserted to complete the data set data imputation.

One common way used in previous studies is to apply the secondary classifier for data editing purposes [6, 9, 10]. The secondary classification logic contains heuristics on whether the minimum data set contains inconsistencies. The applied kernel-based term vector algorithm locates k -nearest neighbours for the query vector, creates association rules for possible values for imputation from the k -nearest neighbours.

The improper values for imputation are filtered using additional logic. Kernel-based algorithms such as term vector analysis are used in high dimen- sional data spaces for calculating distances of two data sets. These distance measurements have been reported to perform similarly in high dimensional data spaces for nearest neighbour queries [3]. Because of the calculation complexity requirements and high dimensional domain data space, this study adopts the term vector approach for locating the nearest neighbours from the existing knowledge bases.

The term vector query is formed from part of the minimum data set that is supposed to be incomplete. This technique is flexible for weighting different parts of the term vector which can be assumed to be appropriate in the domain: some codes are more important than the others. Figure 1 depicts a sample network how a collection of minimum data sets are interlinked by the diagnosis and procedure codes.

Furthermore, the tfij is the within document frequency indicating the number of occurrences of term tj in document i. These equations can be used to flexibly modify the weight of a particular term in the query vector. Furthermore, some data values can be left out from the query vector depending on the type of data that is being searched for. From the result set sorted with the ranking mechanism, k -nearest neighbours are collected.

From this set, an association rule table is formed to describe values and probabilities for imputation. Depending on the use case, different sorts of probability distributions can be utilized in the creation of the association rule table. A simple way is to search all terms from the k -nearest neighbours that do not exist in the original query vector Q and sort these terms based on their frequency within the neighbours.

The resulting values in the association rule table can be further filtered using case specific logic, such as the secondary classifier, to achieve proper values for imputation. However, for the accurate nearest neighbour query, the query vector must be carefully selected.

The query vector accuracy would benefit if the principal component could be identified from the data set. The principal component analysis as a general information processing topic can be time consuming especially in extremely high dimensional data spaces. Therefore it is convenient to provide the algorithm with additional information about the semantics of the data.

Since the values in the classified data sets correspond to a well-defined coding scheme, this sort of additional knowledge can be provided for the query mecha- nism. For instance in the case of minimum data sets surgical procedures can be given a greater weight than the codes representing blood samples. Furthermore, external weights and price lists can be utilized to further empower the query vector.

As discussed, the proposed algorithm can be used for different purposes to impute different types of data. In this study, the algorithm was tested for imputing primary diagnoses for minimum data sets containing a surgical procedure. Previous studies have noted that surgical pro- cedures are coded more accurately than the diagnosis codes [2, 11]. However, since the currently used secondary classification is heavily based on the diag- nosis coding, it is possible that patient episodes that contain expensive surgical operations end up in an inappropriate patient group if the primary diagnosis is incorrect.

Since the proposed data imputation algorithm utilizes machine learning techniques, the algorithm needs to be trained with a knowledge base before it can be used. In this study the algorithm was trained using several different materials from Finnish hospital districts. These materials represent inpatient material of varying time periods.

Together the training set contained several hundreds of thousands of minimum data sets. First, a subset was created from the material with the selection criteria that the minimum data sets contained a surgical procedure. This sub-material was corrupted by removing all primary diagnoses. The algorithm was then applied for primary diagnosis imputation. The original material was used as a golden standard to evaluate the accuracy of the results. The imputation accuracy was evaluated using different classification granularity levels.

As discussed, the diagnosis classification is very fine grained containing over 12 codes. There- fore the imputation accuracy is measured in Figure 2 using various classification granularity levels. These granularity levels include the fine grained diagnosis code, denoted by DG in Figure 2, and coarse grained diagnosis codes with four, three and two character precision. As can be seen from Figure 2, the imputation accuracy depends on the clas- sification granularity level. This result is probably due to the sparse data space: the granularity of the classification instruments are overwhelming compared to the number of real data sets.

For the most fine granular diagnosis classification the imputation accuracy is The accuracy rises for the coarse grained diagnosis classifications: for instance for the two character level diagnosis codes the accuracy is From Figure 2 it can be noted that for the MDC and DRG, that are used for secondary purposes, the accuracy is more precise than for primary classifica- tion. For MDC the primary diagnosis imputation accuracy is For the DRG the corresponding accuracies are The data space is high dimensional and there are several aspects that were not addressed in this study.

One of these aspects is time. The occurrence of some diseases varies depending on the time of year. Another issue is data editing, i. The proposed imputation algorithm relies on machine learning principles. For accurate imputation the algorithm should be trained using a golden standard, i. It is clear that several hundreds of thousands of min- imum data sets do not cover the data space thoroughly. It can also be assumed that the imputation results would be more accurate if the training database would contain more data.

Furthermore, in the context of minimum data sets the golden standard should be created using several independent healthcare pro- fessionals who would evaluate each data set separately. However, with such a method it is impossible to create a golden standard that would cover such a high dimensional data space. Therefore, in this study the golden standard is normal data extracted from hospitals without extra evaluation about the quality of the data.

Previous studies have reported various types of problems in the minimum data sets that are collected in Finland and similar problems can be expected to Table 1: Incorrect primary diagnosis Minimum Data Set Value Label Primary Diagnosis O Therefore it is clear that there are minimum data sets in which the primary diagnosis is incorrect in the golden standard used in this study.

An example case illustrating this methodological issue is listed in Table 1, which lists an example minimum dataset in which the primary diagnosis is incor- rect. However, the per- formed procedure code MBA00 indicates that during care a normal delivery or an abortion has been performed on the patient. In either case, a new primary diagnosis should be given for the patient, since the first diagnosis is not the reason for the patient care.

However, for some reason a new diagnosis has not been assigned and the case listed in Table 1 cannot be corrected without adding information to the record. When the primary diagnosis is removed from the patient case listed in Table 1 and imputed with implemented algorithm, the first imputation is the diagnosis code O The imputed minimum data set is listed in Table 2. It is clinically clear that this code is more correct as primary diagnosis than the original primary diagnosis O However, as the original material is used as the golden standard, this case is marked as incorrectly imputed.

Secondary processes tend to rely on the data that is pro- duced by the primary processes. Missing data values distort secondary business processes since the data gathered does not accurately reflect relevant aspects of the primary processes. This study proposed a registry based machine learning algorithm for coded data imputation. The algorithm was tested with minimum data sets from health- care. The preliminary results show that the imputation accuracy may be suffi- cient for secondary processes that apply different sorts of data aggregations and Table 2: Imputed primary diagnosis Minimum Data Set Value Label Primary Diagnosis O With minimum data sets, the secondary classification accuracy was This study applied external knowledge about the semantics of the procedure classification to identify principal components.

It can be anticipated that there are many cases, in which instead of a single principal component such as an expensive surgical procedure, there are multiple minor observations that are relevant for the imputation. Aro, R. Koskinen, and I.

Sairaalastapoistorekisterin diagnoosi-, toimenpide- ja tapaturmatietojen luotettavuus. Duodecim , pages —, Colin, R. Ecochard, F. Delahaye, G. Landrivon, P. Messy, E. Morgon, and Y. Data quality in a drg-based information system. Interna- tional Journal for Quality in Health Care, 6 —, Sural, Y. Gu, and S. Similarity between euclidean and cosine angle distance for nearest neighbor queries.

ISBN: Hakkinen U. Jarvelin J Pekurinen M. Junnila M. Sairaaloiden tuottavuuden kehitys ISSN: PhD thesis, University of Helsinki, Rauhala and M. Coding of diagnoses in finnish specialised health care - do the statistics reflect medical or coding practices?

Finnish Medical Journal, 32 62 —, Article is in Finnish. Resnik, M. Niv, M. Nossal, G. Schnitzer, J. Stoner, A. Kapit, and R. Using intrinsic and extrinsic metrics to evaluate accuracy and facilitation in computer-assisted coding. American Health Information Management Association, Num- ber Number Stausberg, P.

Med, D. Koch, J. Ingenerf, R. Nat, and Betzler. Com- paring paper-based with electronic patient records: Lessons learned during a study on diagnosis and procedure codes. Journal of the American Medical Informatics Association, 10 —, Tufts-Conrad, A. Zincir-Heywood, and D. Som - feature extraction from patient discharge summaries. In Symposium on Applied Computing, pages — ACM, Wilkinson and P.

Using the cosine measure in a neural network for document retrieval. ACM, pages —, Box 20, Lappeenranta - Finland Abstract. Electricity spot market price is notoriously difficult to pre- dict because of the high variability of its volatility that results in promi- nent price spikes, interlaced with more Gaussian behavior. Most energy producers try to keep flexibility between different energy sources, mostly to diversify raw materials price risk.

Table 1 presents repartition of electric energy origins among the Scandinavian countries. Table 1: Different types of energy sources in Scandinavia. Electricity prices on real-time markets are both highly volatile and difficult to predict. However, ongoing analyses of spot markets are conducted in order to make markets as close to perfect as possible.

The main obstacle is that techniques of calculating electricity prices differ significantly in different countries. Nevertheless, the aim is to set the prices based on day-ahead and hour-ahead orders, so that the balance between supply and demand is met. Firstly, they are highly correlated with temperature and hydrological conditions - the higher is the precipitation, the cheaper is electricity.

Secondly, the prices are extremely dependent on demand. When power generation is below the adequate level, prices rise. This forces buyers to consume less and suppliers to increase production. When supply is sufficient, prices drop, resulting in lower power generation and ordinary consumption levels.

Spot markets are exchange markets where the exchange of takes place within up to two working days after striking a deal. This characterizes equally share, bond, currency and commodity exchanges. Electricity trading is one of the most significant spot markets. However, there is one main feature which distinguishes electricity from other types of exchangeable stock.

Usually differences between demand and supply can be managed by storage capacity. Unfortunately, elec- tricity is something that cannot be kept in a warehouse. In this manner, spot trading provides a possibility of almost permanent balance between supply and demand. The main goal was to create a common Nordic market with a guarantee of strong compe- tition between suppliers in the area.

That was possible due to a wide diversity of Scandinavian energy sources: hydropower Norway, Sweden, Finland , nu- clear power Sweden, Finland , thermal power Sweden, Finland, Denmark and significantly increasing wind power Denmark. A strict daily schedule is obligatory for all market participants. Though it is a corporation not a stock exchange its most important role is to provide spot market trading, which will match electric power supply and demand. Moreover, the Pools are of a not-for-profit character.

Their goal is to work out electricity prices in order to match demand and supply. In addition they have strict policies forbidding any professional connections between employees and companies trading in the Pools. Their unique features emerge from the impossibility of storing electric energy. In practice, these assumptions are hard to verify and one often resorts to empirical trial and error in finding a suitable model and hoping that the residuals it leaves do not display any significant structure.

More recently, it has become computationally possible to study the validity of such assumptions by Monte Carlo simulation. A particularly appropriate variant is the Markov Chain Monte Carlo method that can be used to study the covari- ance of model parameters as well as the robustness of its forecasts by treating ARMA and GARCH model parameters as samples from some distribution.

Since returns of electricity price data shows heteroscedasticity, i. These types of models are widely used for time series that have variance varying with time. Financial data sets are often characterized by so- called variance clustering [2, 4], which means noticeable periods of higher and lower disturbances in the series.

An autoregressive conditional heteroscedasticity model represents the vari- ance of a current error term as a function of variances of error terms at previous time periods. ARCH simply describes the error variance by the square of error at a previous period. Moreover, for estimation in het- eroscedastic models a maximum likelihood method unlike to ARMA methods needs to be employed instead of ordinary least squares.

These techniques can be extended up to several estimates in any given model. MCMC techniques are also used to construct the distributions of unknown parameters based on random variables generated from specific well known distributions, as described in a Bayesian formulation of any problem [5]. MC methods are used to sample random numbers from different probability distributions. When one wants to study a particular problem, an MCMC method is con- structed in such way that it generates a random sample from given distribu- tions.

A good selection of the prior distribu- tion results in the best parameters known to be more probable than others. In Markov Chain Monte Carlo methods, the main idea is to create a Markov Chain using random sampling so that the created chain has the posterior dis- tribution as its unique stationary distribution, i. On the other hand, when the proposal distri- bution is too narrow, the acceptance ratio is high but a representative sample of the target distribution is achieved slowly.

A very practical way for solving this issue takes the previously simulated value into account when the proposal is constructed. Else, stop the algorithm [5]. In the algorithm the proposal width is the covariance matrix C of the Gaus- sian proposal distribution, or variance in one dimensional case. The problem of how to choose a proposal distribution is now transformed into the problem of choosing the covariance matrix C so that the sampling is efficient.

But recently, some new techniques based on modifications of the Metropolis algorithm have been introduced in order to up- date the covariance matrix, like adaptive proposal AP and adaptive Metropolis AM [6]. The covariance matrix of the Gaussian proposal can be chosen by trial and error. However, it is useful to use the covariance approximation obtained from linearization.

Where X is a vector of all control variables in the model. Finally, we compare the standard errors associated to the esti- mated parameters with MCMC errors and test the reliability of forecasts by comparing MCMC simulated predictions to original data. NEPool data set of daily prices lasts over days, so nearly 7 years.

Prices '! We can see that both sets are build of clusters with different variation of amplitude. Peaks are common components of energy spot prices. Due to their appearances, such signals are difficult to estimate by basic mathematical tools.

Peaks are undesired because of their non-differentiable nature. Use of Stochas- tic Differential Equations is impossible and one has to address this problem with methods of discrete type. These functions are depicted in Figure 2. As we can see both correlation and partial correlation at different lags are not very high and reach The later checks if a signal includes ARMA effects.

Here by standardized residuals we mean the innovations divided by their conditional standard deviation. After generating parameter chains with a length of , we study their pair wise joint distributions, to reveal possible correlation between estimated param- eters. This fact shows a significant level of correlation. Given results violate MCMC assumptions that require model parameters to be uncorrelated. These do not follow MCMC theory either — distributions of some parameters appear to be non-Gaussian for both models.

One reason to the non-Gaussian distribution reflected by parameter covari- ance is the constraint of non-negativity imposed upon most parameters, which were bounding ranges of prior distributions. In case of NEPool spot market, a predictive distribution was constructed based on the sampled values for model prediction in terms of price returns, where 22 values were predicted as shown in Figure 7. Figure 7 shows that the predictive distribution for the price returns will most likely lie inside the calculated bounds.

However, we can see that the longer the forecasting horizon is, the more uncertainty predicted values have. On the other hand, the posterior distribution of the forecast is concentrated around the initial prediction. This conclusion stems from comparison of random variations of the predictive distribution of returns and the original return series.

Analogically, comparison of predictive distribution for portfolio returns and original returns indicates that a GARCH 2,1 model for NORDPool can also be used for forecasting the returns for a short-term horizon, as shown in Figure 8. On the other hand, the fact that the true time series does not lie within the posterior distribution of GARCH forecasts means that there must be some essential feature in electricity spot price time series not captured by the GARCH paradigm, and by implication not by any ARMA model either.

In summary, even though some MCMC assumptions were violated, shapes of predictive distributions for model coefficients confirmed the initial prediction of their values. They also indicated that both estimated models may work reasonably in short-term forecasting. Both the size of the data sets, and the behavior of the two time series are quite different, even if both series display prominent spikes.

The results of the MCMC analysis indicate that although the models are able to forecast the future behavior of spot market prices with some skill, the models are not well identifiable. This is shown in the non-Gaussian structure of model parameter covariance, and also in the escape shown by the true spot price from the confidence envelope provide by MCMC sampling of model parameters. Such results indicate that the behavior of electricity spot price is not captured by just adding the assumption of heteroschedasticity - there must be something deeper at play.

In fact, other research groups have come to the same conclusion by different means, such as Bottazzi, Sapio and Secchi [9]. They study the Subbotin family of distributions and similarly identify that NordPool time series needs at least two different distributions to capture its dynamics.

Indeed, it appears as if the price time series would obey two different dynam- ics. Such a dual market nature would call for at least two different models to be used simultaneously. References [1] Aleksander Weron and Rafa! Power Exchange: Risk management strategies. CIRE, Wroc! Introductory econometrics for finance. Cambridge University Press, United Kingdom, John Wiley and Sons, United States, Nonstationary nonlinear heteroskedasticity.

Journal of Econometrics, —, October Monte carlo methods in parameter estimation of nonlinear models. Autoregressive conditional heteroscedasticity with estimates of the vari- ance of united kingdom inflation. Econometrica, —, Box, Gwilym M. Jenkins, and Gregory C.

Time series analysis: forecasting and control. Prentice-Hall, Englewood Cliffs, 3 edition, Some statistical investigations on the nature and dynamics of electricity prices. Physica A, —61, Guillen1 and I. Rojas2 and G. Rubio2 and H. Pomares2 and L. Herrera2 and J. The interface allows programmers and researchers to design parallel algorithms with the MATLAB application using all its advantages. The new interface is com- pared with other approaches showing smaller latency times in communi- cations and an application to an algorithm to design RBFNN for function approximation is shown in the experimental results.

More concretely, the Message Passing Interface standard, which is one of the most used libraries in parallel programming, is not supported. The Message Passing Interface was designed in order to provide a program- ming library for inter-process communication in computer networks, which could be formed by heterogeneous computers. NET, python, Ocaml, etc. This file contains all the. When the application is run for the first time, the content of this file is decompressed and a new directory is generated.

The process that MATLAB follows to generate a stand-alone application is made automatically and totally transparent to the user so he only has to specify the. This whole process is depicted in Figure 1. As listed above, there is a code generation step where an interface for the MCR is created. This toolbox has become quite popular, showing the increasing interest of the fusion between MATLAB and the emerging parallel applications.

This is the main reason why the new interface proposed in this paper was developed. The file that has the. Once the code is written using these special functions, it has to be compiled using the MATLAB mex compiler that generates an specific.

The result is that the deployed application can start the MPI environment and call all the routines defined by the standard. The process to generate a stand-alone application that uses MPI is shown in Figure 2. As the interface has been coded in a single file MPI. Al- though the MPI Comm size and MPI Comm rank are included in this called, they can be invoked separately using other communicators as MPI allows to define different communica- tors, assigning different ranks to the same process.

To show this efficiency gain, the new interface was compared to a previous one. Output Parameters buf Initial address of receive buffer choice. A simple program that performs a Send and Recv between two processes running in two processors was implemented. The program was executed times and on each run, the time elapsed during the MPI function calls was measured.

As the results show, there is a larger overhead time when using the other MPI interface than when using the new one. This is the consequence of performing an unique call to a mexfile as explained in the subsection above. As the size of the packet increases, the overhead time becomes imperceptible, however, for fine grained applications where there exists many communications steps, this overhead time can become crucial for the application to be fasted.

The algorithm presented in [4] was implemented using the new interface so it was possible to be executed in a Sun Fire E15K. The bandwith of the Sun Fire can reach The functional parallelism makes reference to the one that can be obtained when distributing the different tasks in several processes so, as was demonstrated in [6, 7], this kind of parallelism increases the efficiency and improves the results.

The data parallelism can be applied to genetic algorithms from two perspectives: the data could be the individuals or the input for the problem. In this case, the first one was considered so an initial population of individuals was processed by a initial set of three specialized island.

The algorithm was executed using a synthetic function to be approximated and the execution times are shown in Table 3 and in Figure 5. The speedup obtained thanks to the parallelism is represented in Figure 6. Execution time 3 Proc. The benefits of this new interface in comparison with previous ones is that it is possible to use it independently of the platform the application will be run on, the implementation of the MPI standard and it also has smaller overhead times.

All these is translated in a better perfomance when building models such as RBFNN for time series prediction, regression or function approximations. References [1] J. Anguita, E. Ros, and J. SCE Toolboxes for the development of high-level parallel applications. Lecture Notes in Computer Science, —, Pomares, J.

Rojas, L. Herrera, and A. Lecture Notes in Artificial Intelligence, —, Park and I. Approximation and Radial Basis Function Networks. Neural Computation, —, Rojas, J. Herrera, and B. Lecture Notes in Artificial Intelligence, Predicting time series necessitates choosing adequate re- gressors.

For this purpose, prior knowledge of the data is required. By projecting the series on a low-dimensional space, the visualization of the regressors helps to extract relevant information. However, when the series includes some periodicity, the structure of the time series is better pro- jected on a sphere than on an Euclidean space. This paper shows how to project time series regressors on a sphere. A user-defined parameter is introduced in a pairwise distance criterion to control the trade-off between trustworthiness and continuity.

Moreover, the theory of optimization on manifolds is used to minimize this criterion on a sphere. Con- ceptually, traditional methods [1, 2, 3] use the past values of a time series to predict future ones; these methods fit a linear or a nonlinear model between the vectors that gather the past values of the series, the regressors, and the values that have to be predicted.

Note that exogenous variables and prediction errors may be used as inputs to the model too. A first difficulty encountered by these methods is the choice of a suitable regressor size. Indeed, the regressors have to contain the useful information to allow a good prediction [4]. If the regressor size is too small, the information contained in the vector yields a poor prediction. Conversely, with oversized regressors, there can be redundancies such that the methods will overfit and predict the noise of the series.

For this reason and many other ones, including the choice of the model itself, it is useful to visualize the data here the regressors for a preliminary understanding before using them for prediction. This can be achieved by data projection methods [5, 6, 7, 8] which are aimed at representing high-dimensional data in a lower dimensional space. The projection of the regressors makes, for example, easier the visualization of some peculiarity in the time series.

Onclinx is funded by a grant from the Belgium F. The scientific responsibility rests with its author s. The au- thors thank Prof. Pierre-Antoine Absil for his suggestions on the theory of optimization on manifolds. In a first step, oversized regressors are projected to remove their potential redundancies and to reduce the noise. Most distance-based projection methods define the loss of information by the preservation of the pairwise distances. How- ever, projection methods have to deal with a trade-off between trustworthiness and continuity [9], respectively the risk of flattening and tearing the projection.

To control these types of behaviour, a user-defined parameter is introduced in the criterion [10] that implements the trade-off and that allows its control. Furthermore, when time series have a periodic behaviour, it is difficult to embed them in an Euclidean space because of their complex structure [11]. In- deed, let us assume that the oversized regressors are lying close to an unknown manifold embedded in a high-dimensional space.

Since the series is periodic, the manifold probably intercepts itself. In this context, the choice of a suitable projection manifold is motivated by its ability to keep the loops observed in the original space; the quality of the projection relies on its ability to preserve the global topology underlying the data distribution. The constraint of preserving loops is widely used in the context of topology-based projection methods, as the Self-organizing maps, where spheres [12, 13] and tori [14] are often used as projection manifolds; this paper presents a distance-based projection method on a sphere, a manifold that allows loops in the projection space.

The projection is achieved by the minimization of the pairwise distance crite- rion presented in Section 2. Since the projection space is non-Euclidean, Section 3 presents an adequate optimization procedure. Next to a brief introduction of the theory of optimization on manifolds [15], the theory is adapted to project data on a sphere. The projection of a sea temperature series on a sphere is presented in Section 4. In order to take into consideration the advantages of the projection on mani- folds, the forecasting methods should be adapted such that the prediction of time series can be based on the projected regressors.

Section 4. By projecting the regressors on a sphere, a new projected time series is defined on the sphere; this series can easily be predicted using the Optimal-Pruned Learning Machine method [16]. Following these first results, the original time series is predicted with the projected regressors; the results of the forecasting are compared with the prediction of the series based on a dimensional oversized regressors. As previously mentioned, data projection methods have to deal with a trade-off between trustworthiness and continuity.

Having in mind the compromise to reach these two objectives, a pairwise criterion can then be defined without restriction on the structure of the manifold. Assuming that data close to a cylinder must be projected on the two-dimen- sional Euclidean space, a first option is to cut the cylinder along a generating line and to unfold it on the R2 Euclidean space.

The resulting projection is trustworthy since two data that are close in the projected space R2 are also close in the original space the cylinder. However, because the cylinder has been torn, the projection cannot be continuous. A second option is to flatten the cylinder to preserve the continuity. Actually, two data that are close in the original space, the cylinder, remain close in the projected one; the projection is thus continuous.

Nevertheless, this projection is no more trustworthy since data coming from opposite part of the cylinder may be projected close from each other. By counting the points that are close in one space but not in the other space, the trustworthiness and the continuity quality measures [9] are intuitively de- fined.

Nevertheless, these measures are discrete and the optimization of these criteria is therefore difficult. The minimization of the unweighted cost function N! In the projection context, this situation is against the intuition; one prefers to preserve the pairwise distances between close data rather than minimizing f.

By dividing each term of the cost function by the original distance Dij , the minimization of the tearing error favours the continuity of the projection. Indeed, if it happens that two original data are close despite they are faraway in the projected space, they will dominate. Therefore, the minimization of the following cost function tends to make these data closer in the projected space: N!

Be- cause the projected points have to lie on a manifold, traditional optimization procedures cannot be used; the theory of optimization on manifolds proposes a powerful alternative. After an introduction to the topics from the theory of op- timization on manifolds, adaptations to project data on a sphere are presented.

One could argue that to perform an optimization while keeping the projected points on a sphere, it is possible to perform a standard optimization in the spheri- cal coordinate space. Unfortunately, this is not true since there are singularities in the two poles of the sphere.

Actually, these two points are represented by two segments in the spherical coordinate space. To circumvent these difficulties, the theory of optimization on manifolds pro- poses to consider the problem as an unconstrained minimization problem but by taking in mind that each point has to stay on the manifold all along the optimization procedure [15]. Working on a manifold does not allow movements through straight lines, as it is the case in the steepest descent gradient method; the curves of the manifold can however replace these straight directions since they include the curvature of the manifold and its global topology.

Searching for a minimum of a cost function f can be achieved by adapted line- search algorithm. Nevertheless, this direction may point faraway from the manifold. However, this location is not on the manifold; it has then to be retracted on the latter.

For details of the propose line-search algorithm see [15]. First, one has to define the manifold M and the tangent space Ty M. In addition to the spherical form of the manifold, one has also to add its radius R. The value of the radius is a scaling factor; this means that the radius R is considered as a parameter of the manifold because the adequate sphere is not known a priori.

Finally, if the angle between the vectors yi and yj is known, the product between the radius and this angle defines the distance between yi and yj. In order to evaluate this angle, the geodesic distance between yi and yj on the yi! The series is represented in Fig.

The series contains temperature measures; a yearly periodicity can easily be observed. The size of the regressors is chosen experimentally with respect to the length of a single period: dimensional oversized regressors are built. Even if they probably contain all useful infor- mation for the prediction, these regressors are noisy and they certainly contain redundancies. The regressors are thus projected on a sphere according to the above methodology.

The forecasting of the time series is, at the end, based on the projected regressors. Finally, the prediction of the original time series is performed and evaluated. Both the prediction of the projected time series on the sphere, and the prediction of the original time series based on the projected regressors, use the OPELM method [16].

The geodesic distance in the high-dimensional space Dij is approximated by the shortest path in the graph built through the 50 closest neighbours [17, 18]. The colours used are the same as in Fig. The additional curve in Fig. The projected time series turns around the sphere such that the sphere keeps the periodicity of the time series.

Furthermore, the isolated part of the projected data in the upper left region of the sphere in Fig. In Fig. According to both Fig. This subsection shows how the projected regressors can be used. Let us consider the projected time series defined by the locations y t on the sphere, with t between 1 and N.

OPELM is a two-layer regression model, where the first layer is chosen randomly among a set of possible activation functions and kernels, and the second layer is optimized with linear tools. The speed of optimizing such models makes it possible to test a large number of them, among which the best according to some validation criterion is selected.

The learning set is randomly built with 66 percent of the initial set; simulations are performed in order to estimate the learning and the validation errors as average over all the experiments. The results are shown in Fig. However, this result does not mean that the original series can be easily predicted too. As a first attempt in this direction, we propose to build another prediction model based on the projected regressors. In [20], the authors define new re- gressors by concatenating the projected regressors with the corresponding value x t.

Here, we use an alternative idea, which consists in predicting the variations in the time series using the projected regressors. In this figure the learning error of the prediction based on the projected regressors is higher than the learning error based on the dimensional initial regressors, but the validation error is lower when using the projection.

This is likely to be due to overfitting of the model based on the dimensional regressors. The method minimizes a pairwise distance cost function where the trade-off bet- ween trustworthiness and continuity is controlled by a user-defined parameter. The projection on a sphere is aimed at embedding the periodicity of time series using a dedicated optimization method.

The quality of the projection is assessed through the trustworthiness and the continuity quality measures and is compared to the same measures obtained after projecting on Euclidean spaces. The projected regressors can be used to forecast the original time series. Nevertheless, the OPELM prediction method is not specifically adapted to spherical data for which the manifold contains another part of useful information.

This will be studied in future work. References [1] G. Time Series Analysis : Forecasting and Control. Holden-Day, Incorporated, System Identification, Theory for the user. Chatfield and A. Time series prediction: Forecasting the future and un- derstanding the past. International Journal of Forecasting, 10 1 —, June On the numerical determination of the dimension of an attractor. In Dynamical Systems and Bifurcations.

Groningen, Lee and M. Nonlinear Dimensionality Reduction. Belkin and P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. Brun, C. Westin, M. Herberthson, and H. Fast manifold learning based on riemannian normal coordinates. Springer, Nonlinear projection with the isotop method. Dor- ronsoro ed. Venna and S.

Neighborhood preservation in nonlinear projection methods: An experimental study. Springer- Verlag. Local multidimensional scaling with controlled tradeoff be- tween trustworthiness and continuity. WSOM, September Wertz, and M. Nonlinear data projection on a sphere with a controlled trade-off between trustworthiness and continuity.

ESANN, d-side publi. Self-organizing maps on non-euclidean spaces. Oja and E. Kaski, editors, Kohonen Maps, pages 97— Elsevier, Amsterdam, Nishio, Md. Altaf-Ul-Amin, K. Kurokawa, K. Minato, and S. Spherical som with arbitrary number of neurons and measure of suitability. Visualization of high-dimensional data with relational perspective map. Infor- mation Visualization, 3 1 —59, Absil, R. Mahony, and R. Optimization Algorithms on Matrix Manifolds.

Miche, P. Bas, C. Jutten, O. Simula, and A. A methodology for building regression models using extreme learning machine: Op-elm. Lee, A. Lendasse, and M.

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Thank you for - Others. Recently, one of add external formatter us and reported. Integrations eM Client contacted by e-mail made to Thunderbird and your users, access connections are.The conditional mean offset is zero by default. To estimate the offset, specify that it is NaN. EstMdl is a fully specified garch model object. That is, it does not contain NaN values. You can assess the adequacy of the model by generating residuals using infer , and then analyzing them. To simulate conditional variances or responses, pass EstMdl to simulate. To forecast innovations, pass EstMdl to forecast. Simulate conditional variance or response paths from a fully specified garch model object.

That is, simulate from an estimated garch model or a known garch model in which you specify all parameter values. Fit the model to the annual nominal return series. Simulate paths of conditional variances and responses for each period from the estimated GARCH model. Rows correspond to a sample period, and columns correspond to a simulated path. Plot the average and the Compare the simulation statistics to the original data.

Forecast conditional variances from a fully specified garch model object. That is, forecast from an estimated garch model or a known garch model in which you specify all parameter values. Create a GARCH 1,1 model with an unknown conditional mean offset, and fit the model to the annual, nominal return series. Forecast the conditional variance of the nominal return series 10 years into the future using the estimated GARCH model.

Specify the entire returns series as presample observations. The software infers presample conditional variances using the presample observations and the model. Plot the forecasted conditional variances of the nominal returns. Compare the forecasts to the observed conditional variances.

A GARCH model is a dynamic model that addresses conditional heteroscedasticity, or volatility clustering, in an innovations process. Volatility clustering occurs when an innovations process does not exhibit significant autocorrelation, but the variance of the process changes with time. A GARCH model posits that the current conditional variance is the sum of these linear processes, with coefficients for each term:.

The table shows how the variables correspond to the properties of the garch model object. GARCH models are appropriate when positive and negative shocks of equal magnitude contribute equally to volatility [1]. You can specify a garch model as part of a composition of conditional mean and variance models. For details, see arima. Analysis of Financial Time Series. Choose a web site to get translated content where available and see local events and offers.

Based on your location, we recommend that you select:. Select the China site in Chinese or English for best site performance. Other MathWorks country sites are not optimized for visits from your location. Toggle Main Navigation. Search MathWorks. Open Mobile Search. Off-Canvas Navigation Menu Toggle.

Main Content. This longhand syntax enables you to create more flexible models. Input Arguments expand all The shorthand syntax provides an easy way for you to create model templates that are suitable for unrestricted parameter estimation. Example: garch 1,1 Data Types: double. Q — ARCH polynomial degree nonnegative integer. Properties expand all You can set writable property values when you create the model object by using name-value pair argument syntax, or after you create the model object by using dot notation.

This property is read-only. Otherwise, P is 0. Otherwise, Q is 0. Constant — Conditional variance model constant NaN default positive scalar. Data Types: double. UnconditionalVariance — Model unconditional variance positive scalar. The model unconditional variance, specified as a positive scalar.

Offset — Innovation mean model offset 0 default numeric scalar NaN. Distribution — Conditional probability distribution of innovation process "Gaussian" default "t" structure array. DoF is estimable. Description — Model description string scalar character vector. Example: 'Description','Model 1' Data Types: string char. Note All NaN -valued model parameters, which include coefficients and the t -innovation-distribution degrees of freedom if present , are estimable.

Object Functions estimate Fit conditional variance model to data filter Filter disturbances through conditional variance model forecast Forecast conditional variances from conditional variance models infer Infer conditional variances of conditional variance models simulate Monte Carlo simulation of conditional variance models summarize Display estimation results of conditional variance model.

Open Live Script. Includes a conditional variance model constant Excludes a conditional mean model offset i. A GARCH model posits that the current conditional variance is the sum of these linear processes, with coefficients for each term: Past conditional variances the GARCH component or polynomial Past squared innovations the ARCH component or polynomial Constant offsets for the innovation mean and conditional variance models.

Tips You can specify a garch model as part of a composition of conditional mean and variance models. References [1] Tsay, R. You have a modified version of this example. Do you want to open this example with your edits?

No, overwrite the modified version Yes. Select a Web Site Choose a web site to get translated content where available and see local events and offers. Please check that port is available. Did not start the server. InstallServiceHandlerInternalException: An internal exception occurred inside the install service handler mechanism at com. MvmExecutionException: connector. Any suggestion? Help, please! If matlab is installed with a standalone license, RDP is possible only if matlab is already running on the host.

If we try to start matlab remotely, a license error occours. This is why I'm interested in the floating license May be you can try different remote programs Or this does not help? NoMachine and TeamViewer may be there are other variants are said to be working with standalone license or run matlab manually and use remotely.

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