scholarly journals Evaluating the tillage management direction effects on soil attributes by space series analysis (case study: a semiarid region in Iran)

2021 ◽  
Author(s):  
Saeedeh Marzvan ◽  
Hossein Asadi ◽  
Luis C. Timm ◽  
Klaus Reichardt ◽  
Naser Davatgar
Keyword(s):  
Spatial Variability ◽  
Moving Average ◽  
Land Use Changes ◽  
Series Data ◽  
Slope Position ◽  
Semiarid Region ◽  
Natural Soil ◽  
Autoregressive Moving Average ◽  
Soil Attributes ◽  
Series Analysis

Abstract The assessment of soil attributes affected by land use changes or different cultivation management strategies is commonly based on a comparison between agricultural fields, neglecting the natural soil spatial variability. This study aimed to develop a methodology based on improved space series to differentiate between spatial variability of soil attributes and the effect of tillage direction when the evaluation is based on comparison between adjacent fields. The study area consists of two adjacent fields of different tillage directions, i.e. up-down tillage (UDT) and contour tillage (COT). Soil sampling was performed at 40 points in each filed at 5 m intervals along a contour line at the mid-slope position. All measured soil attributes, i.e. sand, silt, clay, MWD, GMD, bulk density; SP, CCE, OM, of UDT were significantly (P<0.05) different from those of COT compared by independent sample T test. This analysis could not differentiate between the spatial variability of the soil and the changes induced by tillage. To determine the net effect of UDT on soil attributes, we (i) performed space series analysis on COT data, (ii) used autoregressive, moving average and autoregressive-moving average models to model the space series data on COT field, and (iii) used the best model obtained for each soil attributes on COT to forecast the value of the property in ten adjacent points in the UDT field. Comparison between the forecasted and measured data in UDT showed that the evaluation of tillage direction effect on soil attributes based on comparison between adjacent fields can be over or under estimated when the sampling coordinates and the spatial correlation among adjacent observations of data are ignored. The methodology used was able to differentiate between natural and management induced differences of soil attributes. Overall, the use of this methodology will improve the prediction and understanding of the effects of different cultivation practices on soil quality.

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.

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.

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.

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.

A study on volatility and return spillover of exchange-traded funds and their benchmark indices in India

2019 ◽  
Vol 46 (1) ◽  
pp. 19-39 ◽  
Author(s):  
Buvanesh Chandrasekaran ◽  
Rajesh H. Acharya
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Exchange Traded Funds ◽  
Volatility Spillover ◽  
Conditional Heteroscedasticity ◽  
Content Type ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Practical Implications

Purpose The purpose of this paper is to empirically examine the volatility and return spillover between exchange-traded funds (ETFs) and their respective benchmark indices in India. The paper uses time series data which consist of equity ETF and respective index returns. Design/methodology/approach The study uses autoregressive moving average–generalized autoregressive conditional heteroscedasticity and autoregressive moving average–exponential generalized autoregressive conditional heteroscedasticity models. The study uses data from the inception date of each ETF to December 2016. Findings The findings of the paper confirm that there is unidirectional return spillover from the benchmark index to ETF returns in most of the ETFs. Furthermore, ETF and benchmark index return have volatility persistence and show the presence of asymmetric volatility wherein a negative news has more influence on volatility compared to a positive news. Finally, unlike unidirectional return spillover, there is a bidirectional volatility spillover between ETF and benchmark index return. Practical implications The study has several practical implications for investors and regulators. A positive daily mean return over a fairly long period of time indicates that the passive equity ETFs can be a viable long-term investment option for ordinary investors. A bidirectional volatility spillover between the ETFs and benchmark index returns calls for the attention of the market regulators to examine the reasons for the same. Originality/value ETFs have seen fast growth in the Indian market in recent years. The present study considers the longest period data possible.

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.

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