Health Prognostic Method Based on the Time Series Analysis for Actuators

2013 ◽  
Vol 373-375 ◽  
pp. 329-332 ◽  
Author(s):  
Jing Kai Zhang ◽  
Juan Wang ◽  
Xiao Xiong Liu ◽  
Wei Guo Zhang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Flight Control ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Health State ◽  
Future Health ◽  
Series Analysis ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model

The purpose of health prognostic is to predict the future health status of system and determine the time from the current health state to functional failure completely. Application data time series analysis method often can get the expected prediction effect. Taking into account the failure characteristics of the actuators in flight control system, the autoregressive moving average model is introduced to health prognostic. The prognostic model is established. The simulation results show the effectiveness of the algorithm.

Time series modelling — an integrated approach

1982 ◽  
Vol 19 (A) ◽  
pp. 413-425
Author(s):  
Don McNeil
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Integrated Approach ◽  
Autoregressive Moving Average ◽  
Linear Segment ◽  
Economic Data ◽  
Autoregressive Moving Average Model ◽  
Straight Line ◽  
Time Series Modelling ◽  
Moving Average Model

Some inadequacies of both the traditional (exponential smoothing) and Box-Jenkins approaches to time series forecasting of economic data are investigated. An approach is suggested which integrates these two methodologies. It is based on smoothing the data using straight line segments instead of differencing to obtain stationarity, and forecasting using an autoregressive-moving-average model for the residuals from the most recent linear segment. The efficiency of this approach is calculated theoretically using a series comprising integrated white noise.

scholarly journals A Fuzzy Set-Valued Autoregressive Moving Average Model and Its Applications

Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 324 ◽  
Author(s):  
Dabuxilatu Wang ◽  
Liang Zhang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Analysis ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Complex Data ◽  
Arma Models ◽  
Linguistic Data ◽  
Series Analysis

Autoregressive moving average (ARMA) models are important in many fields and applications, although they are most widely applied in time series analysis. Expanding the ARMA models to the case of various complex data is arguably one of the more challenging problems in time series analysis and mathematical statistics. In this study, we extended the ARMA model to the case of linguistic data that can be modeled by some symmetric fuzzy sets, and where the relations between the linguistic data of the time series can be considered as the ordinary stochastic correlation rather than fuzzy logical relations. Therefore, the concepts of set-valued or interval-valued random variables can be employed, and the notions of Aumann expectation, Fréchet variance, and covariance, as well as standardized process, were used to construct the ARMA model. We firstly determined that the estimators from the least square estimation of the ARMA (1,1) model under some L2 distance between two sets are weakly consistent. Moreover, the justified linguistic data-valued ARMA model was applied to forecast the linguistic monthly Hang Seng Index (HSI) as an empirical analysis. The obtained results from the empirical analysis indicate that the accuracy of the prediction produced from the proposed model is better than that produced from the classical one-order, two-order, three-order autoregressive (AR(1), AR(2), AR(3)) models, as well as the (1,1)-order autoregressive moving average (ARMA(1,1)) model.

scholarly journals Reliability Prediction of Ontology-Based Service Compositions Using Petri Net and Time Series Models

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Jia Li ◽  
Yunni Xia ◽  
Xin Luo
Keyword(s):  
Time Series ◽  
Petri Net ◽  
Response Times ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Firing Rates ◽  
Dynamic Framework ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model ◽  
Service Components

OWL-S, one of the most important Semantic Web service ontologies proposed to date, provides a core ontological framework and guidelines for describing the properties and capabilities of their web services in an unambiguous, computer interpretable form. Predicting the reliability of composite service processes specified in OWL-S allows service users to decide whether the process meets the quantitative quality requirement. In this study, we consider the runtime quality of services to be fluctuating and introduce a dynamic framework to predict the runtime reliability of services specified in OWL-S, employing the Non-Markovian stochastic Petri net (NMSPN) and the time series model. The framework includes the following steps: obtaining the historical response times series of individual service components; fitting these series with a autoregressive-moving-average-model (ARMA for short) and predicting the future firing rates of service components; mapping the OWL-S process into a NMSPN model; employing the predicted firing rates as the model input of NMSPN and calculating the normal completion probability as the reliability estimate. In the case study, a comparison between the static model and our approach based on experimental data is presented and it is shown that our approach achieves higher prediction accuracy.

Modeling and Forecasting Sales Data by Time Series Analysis

1981 ◽  
Vol 18 (1) ◽  
pp. 94-100 ◽  
Author(s):  
S. G. Kapoor ◽  
P. Madhok ◽  
S. M. Wu
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Time Series Modeling ◽  
Average Model ◽  
Sales Data ◽  
Autoregressive Moving Average Model ◽  
Deterministic Function ◽  
Modeling And Forecasting ◽  
Moving Average Model

Time series modeling technique is used to model a series of sales data in which seasonality causes distinct spike peaks. The analysis of actual sales data shows that the seasonality in the data can be approximated by a deterministic function and the stochastic component is a sixth-order autoregressive moving average model. Use of the combined deterministic and stochastic models to derive the minimum mean squared forecast yields reliable results.

Introduction to Time Series Analysis for Organizational Research

2016 ◽  
Vol 20 (1) ◽  
pp. 61-94 ◽  
Author(s):  
Andrew T. Jebb ◽  
Louis Tay
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Moving Average ◽  
Dynamic Change ◽  
Decomposition Methods ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Organizational Research ◽  
Series Analysis

Organizational science has increasingly recognized the need for integrating time into its theories. In parallel, innovations in longitudinal designs and analyses have allowed these theories to be tested. To promote these important advances, the current article introduces time series analysis for organizational research, a set of techniques that has proved essential in many disciplines for understanding dynamic change over time. We begin by describing the various characteristics and components of time series data. Second, we explicate how time series decomposition methods can be used to identify and partition these time series components. Third, we discuss periodogram and spectral analysis for analyzing cycles. Fourth, we discuss the issue of autocorrelation and how different structures of dependency can be identified using graphics and then modeled as autoregressive moving-average (ARMA) processes. Finally, we conclude by describing more time series patterns, the issue of data aggregation, and more sophisticated techniques that were not able to be given proper coverage. Illustrative examples based on topics relevant to organizational research are provided throughout, and a software tutorial in R for these analyses accompanies each section.

Time series modelling — an integrated approach

1982 ◽  
Vol 19 (A) ◽  
pp. 413-425
Author(s):  
Don McNeil
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Integrated Approach ◽  
Autoregressive Moving Average ◽  
Linear Segment ◽  
Economic Data ◽  
Autoregressive Moving Average Model ◽  
Straight Line ◽  
Time Series Modelling ◽  
Moving Average Model

Some inadequacies of both the traditional (exponential smoothing) and Box-Jenkins approaches to time series forecasting of economic data are investigated. An approach is suggested which integrates these two methodologies. It is based on smoothing the data using straight line segments instead of differencing to obtain stationarity, and forecasting using an autoregressive-moving-average model for the residuals from the most recent linear segment. The efficiency of this approach is calculated theoretically using a series comprising integrated white noise.

Estimation and Forecast Performance of a Multivariate Time Series Model of Sales

1980 ◽  
Vol 17 (4) ◽  
pp. 558-565 ◽  
Author(s):  
Mark Moriarty ◽  
Gerald Salamon
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Multivariate Time Series ◽  
Substantial Improvement ◽  
Product Line ◽  
Time Series Model ◽  
Autoregressive Moving Average ◽  
Forecast Performance ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model

A unique form of a multivariate time series model—a “seemingly unrelated autoregressive moving average” model (SURARMA)—is developed in the context of forecasting unit sales of a product in four states. Data from an anonymous firm are used to test the appropriateness of the model and are found to conform to the model's constraints. The model provides substantial improvement in parameter estimation efficiency and forecast performance in comparison with individual state univariate models. SURARMA is potentially relevant to many market forecasting problems involving multiple constituent time series subunits such as states, regions, or products from a product line.

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