Modeling Research on 1982-2000 NDVI Time Series Data of Chinese Different Vegetation Types Based on Autoregressive Moving Average Model

2014 ◽  
Vol 955-959 ◽  
pp. 863-868
Author(s):  
Rong Yu ◽  
Bo Feng Cai ◽  
Xiang Qin Su ◽  
Ya Zi He ◽  
Jing Yang
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Moving Average ◽  
Original Data ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Research Areas ◽  
Noaa Avhrr ◽  
Index Time Series

Vegetation index time series data modeling is widely used in many research areas, such as analysis of environmental change, estimation of crop yield, and the precision of the traditional vegetation index time series data fitting model is lower. This paper conducts the modeling with introducing the autoregressive moving average time series model, and using NOAA/AVHRR normalized differential vegetation index time series data, to estimate the errors of original data which are between under the situation that the parameters to be estimated are lesser, and on the basis gives the fitted equation to the six kinds of main land covers’ vegetation index time series data of Northeast China region.

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.

MODELING POLIO DATA USING THE FIRST ORDER NON-NEGATIVE INTEGER-VALUED AUTOREGRESSIVE, INAR(1), MODEL

2012 ◽  
Vol 09 ◽  
pp. 232-239 ◽  
Author(s):  
TURAJ VAZIFEDAN ◽  
MAHENDRAN SHITAN
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Negative Integer ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Road Accidents ◽  
Arma Process ◽  
Number Of Patients ◽  
Number Of Customers

Time series data may consists of counts, such as the number of road accidents, the number of patients in a certain hospital, the number of customers waiting for service at a certain time and etc. When the value of the observations are large it is usual to use Gaussian Autoregressive Moving Average (ARMA) process to model the time series. However if the observed counts are small, it is not appropriate to use ARMA process to model the observed phenomenon. In such cases we need to model the time series data by using Non-Negative Integer valued Autoregressive (INAR) process. The modeling of counts data is based on the binomial thinning operator. In this paper we illustrate the modeling of counts data using the monthly number of Poliomyelitis data in United States between January 1970 until December 1983. We applied the AR(1), Poisson regression model and INAR(1) model and the suitability of these models were assessed by using the Index of Agreement(I.A.). We found that INAR(1) model is more appropriate in the sense it had a better I.A. and it is natural since the data are counts.

scholarly journals Autocovariance and autocorrelation structures of the generalised autoregressive moving average (GARMA(1,3;δ,1)) model

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Suguneswary Ellappan ◽  
Norhashidah Awang ◽  
Thulasyammal Ramiah Pillai
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arma Model ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Numerical Examples ◽  
New Class ◽  
Autocorrelation Structure ◽  
Special Case

Generalized ARMA (GARMA) model is a new class of model that has been introduced to reveal some unknown features of certain time series data. The objective of this paper is to derive the autocovariance and autocorrelation structure of GARMA(1,3;δ,1)  model in order to study the behaviour of the model. It is shown that the results of this model can be reduced to the autocovariance and autocorrelation of the standard ARMA model as well as a special case. Numerical examples are used to illustrate the behaviour of the autocovariance and autocorrelation at different δ values to show the various structures that the model can represent

scholarly journals Permodelan Produksi Kopi Indonesia dengan Menggunakan Seasonal Autoregressive Integrated Moving Average (SARIMA)

2019 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Hutomo Atman Maulana ◽  
Kasuma Wardany Harahap ◽  
Adriyansyah Adriyansyah ◽  
Rofiroh Rofiroh ◽  
Fuad Zainuddin
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Time Series Data ◽  
Moving Average ◽  
Original Data ◽  
Series Data ◽  
Production Data ◽  
Coffee Production ◽  
Autoregressive Integrated Moving Average ◽  
Seasonal Data

This research used a method in modelling time series data in the form of seasonal data. The method used in this study is the Seasonal Autoregressive Integrated Moving Average (SARIMA). This method is applied to Indonesian coffee production data from January 2009 - December 2013 with the aim of obtaining a model that will be used to predict the amount of coffee production in January 2014 - December 2014. The forecasting results from the next model will be compared with the original data. Data processing is done using EViews software. Based on the results of data processing, the best model for forecasting is obtained, SARIMA (2,1,0) (1,1,1)12

Sign in / Sign up

Export Citation Format

Share Document