Typical Time Series Analysis of Flight Data Based on ARMA Model

Author(s):  
Jianye Zhang ◽  
Peng Zhang
Author(s):  
YU-YUN HSU ◽  
SZE-MAN TSE ◽  
BERLIN WU

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.


Symmetry ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 324 ◽  
Author(s):  
Dabuxilatu Wang ◽  
Liang Zhang

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.


2013 ◽  
Vol 361-363 ◽  
pp. 1604-1610
Author(s):  
Guo Xiong Wu ◽  
Qing Jie Li ◽  
Gao Yun Cheng

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.


2018 ◽  
Vol 7 (2) ◽  
pp. 103-114
Author(s):  
Fachri Faisal ◽  
Pepi Novianti ◽  
Jose Rizal

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.


2011 ◽  
Vol 80-81 ◽  
pp. 516-520
Author(s):  
Han Bing Liu ◽  
Yan Yi Sun ◽  
Yong Chun Cheng ◽  
Ping Jiang ◽  
Yu Bo Jiao

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.


Author(s):  
Kai Liu ◽  
Xi Zhang ◽  
YangQuan Chen

Strong coupling between values at different time that exhibit properties of long range dependence, non-stationary, spiky signals cannot be processed by the conventional time series analysis. The ARFIMA model, which employs the fractional order signal processing techniques, is the generalization of the conventional integer order models — ARIMA and ARMA model. Therefore, it has much wider applications since it could capture both short-range dependence and long range dependence. For now, several software have developed functions dealing with ARFIMA processes. However, it could be a big difference, if using different numerical tools for time series analysis. Time to time, being asked about which tool is suitable for a specific application, the authors decide to carry out this survey to present recapitulative information of the available tools in the literature, in hope of benefiting researchers with different academic backgrounds. In this paper, 4 primary functions concerning simulation, fractional order difference filter, estimation and forecast are compared and evaluated respectively in the different software and informative comments are also provided for selection.


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