Performance Prediction of an Automotive Assembly Line Based on ARMA-ANN Modeling

2014 ◽  
Vol 2 (2) ◽  
pp. 22-39 ◽  
Author(s):  
Annamalai Pandian ◽  
Ahad Ali
Keyword(s):  
Prediction Model ◽  
Assembly Line ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Market Demand ◽  
Robotic Welding ◽  
Bottom Line ◽  
Automotive Body ◽  
Automotive Assembly ◽  
Body In White

This research paper aims to predict the automotive Body-In-White (BIW) robotic welding assembly line performance. A combinational prediction model based on the Autoregressive Moving Average (ARMA) and Artificial Neural Networks (ANN) is developed. Classical methods are often used to predict the assembly line throughput, but not ideal. A combinational prediction model is applied for comprehensive analysis and prediction of the assembly line throughput. The various case studies presented in this paper indicate that the precision of the model is better than the other models. This research has significant practical value to the assembly plant because, based on the prediction, plant can make commitment to achieve the production to meet the market demand. Unpredictable performance of the assembly line in the plant leads to more overtime, less time for maintenance and eventually hurting the company bottom line.

scholarly journals On the Prediction of Upwelling Events at the Colombian Caribbean Coasts from Modis-SST Imagery

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2861 ◽  
Author(s):  
José J. Alonso del Rosario ◽  
Juan M. Vidal Pérez ◽  
Elizabeth Blázquez Gómez
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Models ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Maximum Entropy Spectral Analysis ◽  
Nodal Lines ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Karhunen Loeve Transform

The upwelling cores on the Caribbean Colombian coasts are mainly located at the Peninsula de la Guajira and Cabo de la Aguja. We used monthly averaged Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature as the only information to build up a prediction model for the upwelling events. This comprised two steps: (i) the reduction of the complexity by means of the Karhunen–Loève transform and (ii) a prediction model of time series. Two prediction models were considered: (a) a parametric autoregressive-moving average (ARMA) time series from the Box–Jenkins methodology and (b) a harmonic synthesis model. The harmonic synthesis also comprised of two steps: the maximum entropy spectral analysis and a least-squares harmonic analysis on the set of frequencies. The parametric ARMA time series model failed at the time of prediction with a very narrow range, and it was quite difficult to apply. The harmonic synthesis allowed prediction with a horizon of six months with a correlation of about 0.80. The results can be summarized using the time series of the weights of the different oscillation modes, their spatial structures with the nodal lines, and a high confidence model with a horizon of prediction of about four months.

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.

scholarly journals Analysis and prediction of polar motion using MSSA method

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Polar Motion ◽  
Rotation Axis ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Autoregressive Moving Average ◽  
Rotational Axis ◽  
Autoregressive Moving Average Model ◽  
System Service ◽  
Moving Average Model ◽  
Polar Motion Prediction

AbstractPolar motion is the movement of the Earth's rotational axis relative to its crust, reflecting the influence of the material exchange and mass redistribution of each layer of the Earth on the Earth's rotation axis. To better analyze the temporally varying characteristics of polar motion, multi-channel singular spectrum analysis (MSSA) was used to analyze the EOP 14 C04 series released by the International Earth Rotation and Reference System Service (IERS) from 1962 to 2020, and the amplitude of the Chandler wobbles were found to fluctuate between 20 and 200 mas and decrease significantly over the last 20 years. The amplitude of annual oscillation fluctuated between 60 and 120 mas, and the long-term trend was 3.72 mas/year, moving towards N56.79 °W. To improve prediction of polar motion, the MSSA method combining linear model and autoregressive moving average model was used to predict polar motion with ahead 1 year, repeatedly. Comparing to predictions of IERS Bulletin A, the results show that the proposed method can effectively predict polar motion, and the improvement rates of polar motion prediction for 365 days into the future were approximately 50% on average.

scholarly journals Choosing between identification schemes in noisy-news models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Joshua C. C. Chan ◽  
Eric Eisenstat ◽  
Gary Koop
Keyword(s):  
Bayesian Methods ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Structural Shocks ◽  
Vector Autoregressive ◽  
Identification Scheme ◽  
Identification Schemes ◽  
The One ◽  
Small Models ◽  
Structural Vector

AbstractThis paper is about identifying structural shocks in noisy-news models using structural vector autoregressive moving average (SVARMA) models. We develop a new identification scheme and efficient Bayesian methods for estimating the resulting SVARMA. We discuss how our identification scheme differs from the one which is used in existing theoretical and empirical models. Our main contributions lie in the development of methods for choosing between identification schemes. We estimate specifications with up to 20 variables using US macroeconomic data. We find that our identification scheme is preferred by the data, particularly as the size of the system is increased and that noise shocks generally play a negligible role. However, small models may overstate the importance of noise shocks.

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