ARMA model

This paper introduces a new class of robust estimates for ARMA models. They are M-estimates, but the residuals are computed so the effect of one outlier is limited to the period where it occurs. These estimates are closely related to those based on a robust filter, but they have two important advantages: they are consistent and the asymptotic theory is tractable. We perform a Monte Carlo where we show that these estimates compare favorably with respect to standard M-estimates and to estimates based on a diagnostic procedure.

In this paper, the certainty equivalence principle is used to combine the identification method with a control structure derived from the pole placement problem, which rely on periodic multirate-input controllers. The proposed adaptive pole placers, contain a sampling mechanism with different sampling period to each system input and rely on a periodically varying controller which suitably modulates the sampled outputs and reference signals of the plant under control. Such a control strategy allows us to arbitrarily assign the poles of the sampled closed-loop system in desired locations and does not make assumptions on the plant other than controllability and observability of the continuous and the sampled system, and the knowledge of a set of structural indices, namely the locally minimum controllability indices of the continuous-time plant. An indirect adaptive control scheme is derived, which estimates the unknown plant parameters (and consequently the controller parameters) on-line, from sequential data of the input and outputs of the plant, which are recursively updated within the time limit imposed by a fundamental sampling period T0. Using the proposed algorithm, the controller determination is based on the transformation of the discrete analogous of the system under control to a phase-variable canonical form, prior to the application of the control design procedure. The solution of the problem can, then, be obtained by a quite simple utilization of the concept of state similarity transformation. Known indirect adaptive pole placement schemes usually resort to the computation of dynamic controllers through the solution of a polynomial Diophantine equation, thus introducing high order exogenous dynamics in the control loop. Moreover, in many cases, the solution of the Diophantine equation for a desired set of closed-loop eigenvalues might yield an unstable controller, and the overall adaptive pole placement scheme is unstable with unstable compensators because their outputs are unbounded. The proposed control strategy avoids these problems, since here gain controllers are essentially needed to be designed. Moreover, persistency of excitation and, therefore, parameter convergence, of the continuous-time plant is provided without making any assumption either the richness of the reference signals or on the existence of specific convex sets in which the estimated parameters belong or, finally, on the coprimeness of the polynomials describing the ARMA model, as in known adaptive pole placement schemes.

Accurate prediction of lake-level changes is a very important problem for a wise and sustainable use. In recent years significant lake level fluctuations have occurred and can be related to the climatic change. Such a problem is crucial to the works and decisions related to the water resources and management. This study is aimed to predict future lake levels during hydrometeorological changes and anthropogenic activities taking place in the Lake Eğirdir which is the most important water storage of Lake Region, one of the biggest fresh water lakes of Turkey. For this aim, recurrent neural network (RNN), adaptive network-based fuzzy inference system (ANFIS) as prediction models which have various input structures were constructed and the best fit model was investigated. Also, the classical stochastic models, auto-regressive (AR) and auto-regressive moving average (ARMA) models are generated and compared with RNN and ANFIS models. The performances of the models are examined with the form of numerical and graphical comparisons in addition to some statistic efficiency criteria. The results indicated that the RNN and ANFIS can be applied successfully and provide high accuracy and reliability for lake-level changes than the AR and the ARMA models. Also it was shown that these stochastic models can be used in the lake management policies with the acceptable risk.

The prediction of rainfall is essential for design and management of water resources systems. The objective of this paper is generate monthly rainfall time series and compare three linear stochastic generators for simulation of monthly rainfall at Gharalar rain gauge stations around Urmia, Iran. Monthly rainfall data for thirty years from 1972-2002 were considered for stochastic modeling. Monthly rainfall was

The sample cross-correlation function (SCCF) has been used to study the strength and direction of the linear relation between two jointly stationary stochastic processes. Rosales (2004) and Castano (2005) show that the cross-correlation function between a stationary process and the residuals of an estimated preliminary model can be used as an additional diagnostic tool, for the identification of an appropriate ARMA(p, q) model, for the generating process of the series. The purpose of this article is to show that the

Abrasive waterjet cut surface is characterized using static and dynamic characterization techniques. A novel method of auto regressive moving average model identification called model distance method is utilized here for surface profile and dynamic force characterization. More information about the surface profile generating mechanism is derived through wavelength decomposition of the ARMA models. The dynamic workpiece normal force in abrasive waterjet is influenced by process parameters such as fluctuations in water pressure, change in abrasive flow rate, vibration of the positioning system, traverse speed, nozzle diameter, etc. An attempt has been made in this paper to link the dynamics of the process to the quality of the generated surface. The feasibility of using the dynamic workpiece normal force as a parameter for on-line monitoring of the surface profile generated by abrasive waterjet is also investigated.

Aircraft in-flight test data are typically characterized by short time records due to safety reasons and the fact that the inputs may not be measurable and/or are poorly controllable. In case of quantifiable artificial inputs, the FRFS are contaminated by high environmental noise levels due to the ...

This study tries to understand pricing behavior of firms. In the Post-Keynesian economics, Alfred S. Eichner formed the micro foundation that Eicher(1973) combined previous works and developed micro foundation of Post-Keynesian economics. His model focuses on price setting behavior under the imperfect competition. In this paper, by using wholesale price index of Turkey during the period 1994-2013, ARMA model is fitted. Results demonstrate both moving average and autoregressive patterns in firm’s price setting that findings support coherently expectation of the Post-Keynesian microeconomic foundation.

Modern electricity market design is dominated by locational marginal pricing (LMP) of energy and transmission, coupled with periodic auctions of financial transmission rights (FTRs or TCCs) to hedge congestion price risks. For these market designs to be effective, participants must be able to efficiently discover forward locational prices. With data from monthly TCC auctions in New York, we use time

The purpose of this paper is twofold. Firstly, to investigate the merit of estimating probability density functions rather than level or classification estimations on a one- day-ahead forecasting task of the silver time series. This is done by benchmarking the Gaussian mixture neural network model (as a probability distribution predictor) against two other neural network designs representing a level estimator

The opinions in this work are an exclusive responsibility of his authors and do not necessarily reflect the position of the Central Bank of Argentina. The Working Papers Series from BCRA is composed by papers published with the intention of stimulating the academic debate and ...

Abrasive waterjet cut surface is characterized using static and dynamic characterization techniques. A novel method of auto regressive moving average model identification called model distance method is utilized here for surface profile and dynamic force characterization. More information about the surface profile generating mechanism is derived through wavelength decomposition of the ARMA models. The dynamic workpiece normal force in abrasive waterjet is influenced by process parameters such as fluctuations in water pressure, change in abrasive flow rate, vibration of the positioning system, traverse speed, nozzle diameter, etc. An attempt has been made in this paper to link the dynamics of the process to the quality of the generated surface. The feasibility of using the dynamic workpiece normal force as a parameter for on-line monitoring of the surface profile generated by abrasive waterjet is also investigated.

We consider the decomposition of shocks to a dynamic process into a persistent and a transitory component. Without additional assumptions (such as zero cor-relation) the decomposition of shocks into a persistent and transitory component is indeterminate. The assumption that is conventional in the earnings literature is that there is no correlation. The Beveridge–Nelson decomposition that is widely used in time series analysis assumes a perfect correlation. Without restrictions on the correlation, the persistent-transitory decomposition is only set-identified. For reasonable autoregressive moving average (ARMA) parameters the bounds for widely used objects of interest are very wide. We illustrate that these disquieting findings are of considerable practical importance, using a sample of male workers drawn from the Panel Study of Income Dynamics (PSID).