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 Comparison of GARCH & ARMA Models to Forecasting Exchange Rate

2021 ◽  
Vol 8 (6) ◽  
pp. 979-983
Author(s):  
Meshal Harbi Odah
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Time Series Analysis ◽  
Financial Time Series ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Arma Models ◽  
Series Analysis ◽  
Homogeneity Of Variance ◽  
The Garch Model

Financial time series are defined by their fluctuations, which are characterized by instability or uncertainty, implying that there are periods of volatility followed by periods of relative calm. Therefore, time series analysis requires homogeneity of variance. In this paper, some models used in time series analysis have been studied and applied. Comparison between Autoregressive Moving Average (ARMA) and Generalized Autoregressive Conditionally Heteroscedastic (GARCH) models to identify the efficient model through (MAE, MASE) measures to determine the best forecasting model is studied. The findings show that the models of Generalised Autoregressive Conditional Heteroscedastic are more efficient in forecasting time series of financial. In addition, the GARCH model (1,1) is the best to forecasting exchange rate.

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.

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.

scholarly journals The Application of Spatial Analysis and Time Series in Modeling the Frequency of Earthquake Events in Bengkulu Province

2018 ◽  
Vol 7 (2) ◽  
pp. 103-114
Author(s):  
Fachri Faisal ◽  
Pepi Novianti ◽  
Jose Rizal
Keyword(s):  
Time Series ◽  
Spatial Analysis ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arma Model ◽  
Secondary Data ◽  
Spherical Model ◽  
Kriging Interpolation ◽  
Semivariogram Model ◽  
Series Analysis

This study provides an overview in combining spatial analysis and time series analysis to model the frequency of earthquake. The aim of this research is to apply the spatial statistical analysis and time series analysis in estimating semivariogram parameters for the next four steps. The data in this study is secondary data that has been validated based on sources that publish parameters of earthquake events. Looking at the characteristics of the earthquake frequency frequency data, there are spatial and time elements. The method used in this research is interpolation kriging and Autoregressive Moving Average (ARMA) model. The semivariogram models used in kriging interpolation are: Spherical, Exponential, Gaussian, and Linear. The parameters of the semivariogram model are modeled using ARMA time series analysis adjusted to the model diagnostic results. To measure of fit model is used Mean Square Error (MSE). The result of research is a suitable semivariogram model to be applied in the modeling of earthquake events is the Spherical model. While each parameter is estimated using ARMA model (2,2) with different coefficient estimation value.

The Deformation Prediction of Foundation Pit Slope Based on Time Series Analysis

2011 ◽  
Vol 80-81 ◽  
pp. 516-520
Author(s):  
Han Bing Liu ◽  
Yan Yi Sun ◽  
Yong Chun Cheng ◽  
Ping Jiang ◽  
Yu Bo Jiao
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arma Model ◽  
Information Criterion ◽  
Slope Deformation ◽  
Foundation Pit ◽  
Deformation Prediction ◽  
Series Analysis ◽  
Moving Average Model

Slope stability is the key to ensuring the safety of foundation pit construction. This paper is on the background of metro foundation pit monitoring of the West Railway Station in Changchun City. Through the time series analysis of the pit slope deformation data, the Auto Regressive Moving Average Model (ARMA) of pit slope deformation is established. Then the orders of the model are determined by the Akaike Information Criterion (AIC). Further, the deformation prediction of pit slope is finished using the ARMA model. By the comparison of the predictive value and the true monitoring value, it shows that using time series to analyze the deformation of foundation pit slope is reasonable and reliable. At the same time, this method is providing a new way to estimate the stability of pit slope.

Time Series Analysis for Crime Forecasting Using ARIMA (Autoregressive Integrated Moving Average) Model

2021 ◽  
pp. 158-198
Author(s):  
Neetu Faujdar ◽  
Anant Joshi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Data Visualization ◽  
Moving Average ◽  
Complex Data ◽  
Autoregressive Integrated Moving Average ◽  
Related Data ◽  
Series Analysis ◽  
Internet Users ◽  
Moving Average Model

With massive advancements in the fields of data analysis and data mining, a new importance has been gained by data visualization. Data visualization focuses on visualizing and abstracting complex data to make it comprehensible and easy to understand using visual representation of information. Analysis of crime and crime-related data has been steadily popularizing over the last decade, and this chapter aims at visualizing such data. Crime data for several different types of crime for many countries in the world has been collected, compiled, processed, analyzed, and visualized in this chapter. Predictive analysis of this data has also been performed using time series analysis. This chapter aims to create a hub where internet users can easily view and interpret this data.

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