scholarly journals OPTIMAL RANK-BASED PROCEDURES FOR TIME SERIES ANALYSIS: TESTING AN ARMA MODEL AGAINST OTHER ARMA MODELS

2012 ◽  
Author(s):  
MARC HALLIN ◽  
MADAN L. PURI
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Arma Model ◽  
Arma Models ◽  
Series Analysis

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.

A NEW APPROACH OF BIVARIATE FUZZY TIME SERIES ANALYSIS TO THE FORECASTING OF A STOCK INDEX

2003 ◽  
Vol 11 (06) ◽  
pp. 671-690 ◽  
Author(s):  
YU-YUN HSU ◽  
SZE-MAN TSE ◽  
BERLIN WU
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Trading Volume ◽  
Arma Model ◽  
Future Trend ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
New Approach ◽  
Series Analysis ◽  
Closing Price

In recent years, the innovation and improvement of forecasting techniques have caught more and more attention. Especially, in the fields of financial economics, management planning and control, forecasting provides indispensable information in decision-making process. If we merely use the time series with the closing price array to build a forecasting model, a question that arises is: Can the model exhibit the real case honestly? Since, the daily closing price of a stock index is uncertain and indistinct. A decision for biased future trend may result in the danger of huge lost. Moreover, there are many factors that influence daily closing price, such as trading volume and exchange rate, and so on. In this research, we propose a new approach for a bivariate fuzzy time series analysis and forecasting through fuzzy relation equations. An empirical study on closing price and trading volume of a bivariate fuzzy time series model for Taiwan Weighted Stock Index is constructed. The performance of linguistic forecasting and the comparison with the bivariate ARMA model are also illustrated.

Forecasting of Highway Ecological Environment Evolution Based on Time Series Analysis

2013 ◽  
Vol 361-363 ◽  
pp. 1604-1610
Author(s):  
Guo Xiong Wu ◽  
Qing Jie Li ◽  
Gao Yun Cheng
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Environmental Protection ◽  
Quality Index ◽  
Arma Model ◽  
Ecological Environment ◽  
The Road ◽  
Environment Quality ◽  
Series Analysis ◽  
Environment Evolution

A roads ecological environmental quality index data for years is a time-series, and accurate prediction by exploring the changing rule of the time series is significant for future environmental protection. For this end, based on time series analysis, this paper firstly carries out steady analysis and random test pretreatment for the eco-environment quality index (EQI) data collected. Next, an ARMA model is established by pattern recognition, established bands and parameter estimation, and then verified by the data collected in recent years. Finally, the ARMA model is used to predict the future EQI values. This research will provide effective guidance to the eco-environmental protection along the road.

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.

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