scholarly journals Following a trend with an exponential moving average: Analytical results for a Gaussian model

2014 ◽  
Vol 394 ◽  
pp. 288-303 ◽  
Author(s):  
Denis S. Grebenkov ◽  
Jeremy Serror
Keyword(s):  
Moving Average ◽  
Gaussian Model ◽  
Analytical Results

Estimation of Wave Loading Induced Fatigue Accumulation and Extreme Response of a Container Ship in Severe Seas

2010 ◽  
Author(s):  
Wengang Mao ◽  
Zhiyuan Li ◽  
Thomas Galtier ◽  
Jonas W. Ringsberg ◽  
Igor Rychlik
Keyword(s):  
Narrow Band ◽  
Moving Average ◽  
Gaussian Model ◽  
Numerical Code ◽  
Container Ship ◽  
Counting Approach ◽  
Extreme Response ◽  
Fatigue Estimation ◽  
The Time Domain ◽  
Rainflow Counting

The hydrodynamic analysis of a 4400 TEU container ship with constant forward speed is carried out by the nonlinear numerical code WASIM in the time domain under severe sea states. Straightforward fatigue estimation is performed using the rainflow counting approach based on simulated time series of stresses. The narrow-band approximation, which has been validated in previous work with good accuracy by full-scale measurement of a 2800 TEU container ship, is implemented to estimate the fatigue damage based on the same responses. It is concluded that a slight deviation from the Gaussian process does not influence the fatigue estimation by narrow-band approximation. In addition, extreme response is defined by the level up-crossing approach. The Gaussian crossing model using Rice’s formula is employed to predict the extreme response based on the responses from above numerical analysis. It shows that the Gaussian model is not suitable for this prediction. A more complicated level crossing model is proposed which is based on the Laplace Moving Average method. Its accuracy in prediction of extreme responses is analyzed and presented with good agreement by means of numerical simulations.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

Multidimensional, Nonthreshold Gaussian Model of Source Memory

2004 ◽  
Author(s):  
William P. Banks ◽  
Clayton Stephenson ◽  
Nisha Gottfredson ◽  
Andrew A. Sparrow
Keyword(s):  
Source Memory ◽  
Gaussian Model

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

A novel approach to white liquor control in a continuous digester using the Box-Jenkins methodology to predict chip bulk density

TAPPI Journal ◽  
2015 ◽  
Vol 14 (6) ◽  
pp. 395-402
Author(s):  
FLÁVIO MARCELO CORREIA ◽  
JOSÉ VICENTE HALLAK D’ANGELO ◽  
SUELI APARECIDA MINGOTI
Keyword(s):  
Bulk Density ◽  
Moving Average ◽  
Pulp Mill ◽  
Statistical Decision ◽  
White Liquor ◽  
Novel Approach ◽  
Cooking Process ◽  
Eucalyptus Kraft Pulp ◽  
Relevant Variables ◽  
Kraft Cooking

Alkali charge is one of the most relevant variables in the continuous kraft cooking process. The white liquor mass flow rate can be determined by analyzing the chip bulk density fed to the process. At the mills, the total time for this analysis usually is greater than the residence time in the digester. This can lead to an increasing error in the mass of white liquor added relative to the specified alkali charge. This paper proposes a new approach using the Box-Jenkins methodology to develop a dynamic model for predicting chip bulk density. Industrial data were gathered on 1948 observations over a period of 12 months from a Kamyr continuous digester at a bleached eucalyptus kraft pulp mill in Brazil. Autoregressive integrated moving average (ARIMA) models were evaluated according to different statistical decision criteria, leading to the choice of ARIMA (2,0,2) as the best forecasting model, which was validated against a new dataset gathered during 2 months of operations. A combination of predictors has shown more accurate results compared to those obtained by laboratory analysis, allowing a reduction of around 25% of the chip bulk density error to the alkali addition amount.

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