Hidden Periodic Autoregressive-Moving Average Models in Time Series Data

Biometrika ◽  
1980 ◽  
Vol 67 (2) ◽  
pp. 365 ◽  
Author(s):  
G. C. Tiao ◽  
M. R. Grupe
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Autoregressive Moving Average Models

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.

Statistical modelling of spatial variability of undrained strength

1992 ◽  
Vol 29 (5) ◽  
pp. 721-729 ◽  
Author(s):  
V. Ravi
Keyword(s):  
Time Series ◽  
Spatial Variability ◽  
Computer Program ◽  
Moving Average ◽  
Nonlinear Least Squares ◽  
Autoregressive Moving Average ◽  
Spatial Series ◽  
Moving Average Models ◽  
Undrained Strength ◽  
Autoregressive Moving Average Models

Spatial variability of undrained strength (Cu) has been modelled in several ways in the past. In particular, concepts of time series such as autoregressive moving average models have been used to model the analogous "spatial series" of the values of depth versus undrained strength. It should be noted that the very purpose of such modelling studies is to provide estimates of the values of undrained strength at a given value of depth. In the present paper, the main prerequisite to apply these models, viz. the complete removal of trend present in the spatial series of depth versus Cu, has been focussed. An accurate modelling procedure is recommended which can estimate the values of Cu at a given value of depth better than any other model in this class of models existing in the literature. Sensitivity in the trend patterns of the depth versus Cu data is well taken care of. A computer program has been developed in FORTRAN 77to fit the model in conjunction with a standard nonlinear least-squares routine taken from the literature. One of the advantages of the present model is the speed of convergence of the computer program. Two case studies appearing in the literature have been successfully solved to demonstrate the efficacy of the model developed. Key words : spatial variability, time series analysis, spatial series, nonstationarity, autoregressive moving average models, regression, nonlinear least squares, error sum of squares.

scholarly journals On Modeling Murder Crimes in Nigeria

2018 ◽  
pp. 157-162
Author(s):  
Obubu Maxwell ◽  
Ikediuwa Udoka Chinedu ◽  
Anabike Charles Ifeanyi ◽  
Nwokike Chukwudike C
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Data Set ◽  
Adequate Model ◽  
Resource Center ◽  
Moving Average Models ◽  
Augmented Dickey Fuller ◽  
Auto Regressive

This paper examines the modelling and forecasting Murder crimes using Auto-Regressive Integrated Moving Average models (ARIMA). Twenty-nine years data obtained from Nigeria Information Resource Center were used to make predictions. Among the most effective approaches for analyzing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). The augmented Dickey-Fuller test for unit root was applied to the data set to investigate for Stationarity, the data set was found to be non-stationary hence transformed using first-order differencing to make them Stationary. The Stationarities were confirmed with time series plots. Statistical analysis was performed using GRETL software package from which, ARIMA (0, 1, 0) was found to be the best and adequate model for Murder crimes. Forecasted values suggest that Murder would slightly be on the increase.

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.

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