scholarly journals Deterministic Chaos Detection and Simplicial Local Predictions Applied to Strawberry Production Time Series

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3034
Author(s):  
Juan D. Borrero ◽  
Jesus Mariscal
Keyword(s):  
Time Series ◽  
Initial Conditions ◽  
Moving Average ◽  
Deterministic Chaos ◽  
Autoregressive Moving Average ◽  
Chaos Detection ◽  
Moving Average Models ◽  
Non Linear ◽  
Long Time ◽  
Novel Method

In this work, we attempted to find a non-linear dependency in the time series of strawberry production in Huelva (Spain) using a procedure based on metric tests measuring chaos. This study aims to develop a novel method for yield prediction. To do this, we study the system’s sensitivity to initial conditions (exponential growth of the errors) using the maximal Lyapunov exponent. To check the soundness of its computation on non-stationary and not excessively long time series, we employed the method of over-embedding, apart from repeating the computation with parts of the transformed time series. We determine the existence of deterministic chaos, and we conclude that non-linear techniques from chaos theory are better suited to describe the data than linear techniques such as the ARIMA (autoregressive integrated moving average) or SARIMA (seasonal autoregressive moving average) models. We proceed to predict short-term strawberry production using Lorenz’s Analog Method.

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.

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

scholarly journals Censored time series analysis with autoregressive moving average models

2007 ◽  
Vol 35 (1) ◽  
pp. 151-168 ◽  
Author(s):  
Jung Wook Park ◽  
Marc G. Genton ◽  
Sujit K. Ghosh
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Series Analysis ◽  
Moving Average Models ◽  
Autoregressive Moving Average Models

Prediction method of autoregressive moving average models for uncertain time series

2020 ◽  
Vol 49 (5) ◽  
pp. 546-572 ◽  
Author(s):  
Jingwen Lu ◽  
Jin Peng ◽  
Jinyang Chen ◽  
Kiki Ariyanti Sugeng
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Prediction Method ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Uncertain Time ◽  
Autoregressive Moving Average Models

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

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