scholarly journals ARMA Models to Measure the Scale of Fluctuation from CPT Data

2020 ◽  
Vol 14 (1) ◽  
pp. 230-236
Author(s):  
Brigid Cami ◽  
Sina Javankhoshdel
Keyword(s):  
Spatial Variability ◽  
Moving Average ◽  
Arma Model ◽  
The Other ◽  
True Value ◽  
A Value ◽  
First Case ◽  
Scale Of Fluctuation ◽  
Auto Regressive

Objective: Spatial variability is one of the largest sources of uncertainty in geotechnical applications. This variability is primarily characterized by the scale of fluctuation, a parameter that describes the distance over which the parameters of a material are similar. Spatial variability is generally described with traditional methods of time series analysis. In statistics, the Auto-Regressive Moving Average (ARMA) model is commonly used to describe the relationship between two points in time. Instead of assuming an autocorrelation model, the ARMA model calculates the necessary auto-regressive components (AR), as well as a decaying Mean Structure (MA). The advantage of this method is that it is calculated for each specific field study, so that the data is not forced to fit into a fixed autocorrelation model (e.g. Markovian, Gaussian, etc). Methods: In this study, the ARMA model is introduced as a means of measuring scale of fluctuation, and two case studies and a simulation are used to compare the scale of fluctuation values from the ARMA model to the other estimates. Results: In the first case study, the ARMA model estimated a value of 0.26 m while the other methods ranged from 0.22-0.29 m. In the second case study, the ARMA model estimated a value of 0.40 m while the other methods ranged from 0.40-0.54 m. In the simulated example, where the true value was 5.0 m, the ARMA model estimated a value of 4.73 m while the other methods ranged from 3.24-3.51 m. Conclusion: This paper concludes that ARMA is a promising new method for estimating the scale of fluctuation but requires a considerable amount of research before it can become established in the geotechnical sphere.

Fuzzy Logic–Based Comparison of Predicted Sales Records of Competing Products

2021 ◽  
pp. 097226292097147
Author(s):  
Anuradha Banerjee
Keyword(s):  
Fuzzy Logic ◽  
Moving Average ◽  
Arma Model ◽  
Marketing Strategies ◽  
The Other ◽  
Anova Analysis ◽  
Future Prediction ◽  
Auto Regressive ◽  
Near Future ◽  
One Way Anova

The issue of comparing sales records of competitors is gaining increased importance to both marketing academicians and practitioners to get an idea about approximate trend of customer inclination to their products. Actual sales records of competing products for past few years can be compared in two ways. If sales records exhibit normal distribution, then they can be tested for dominance over the other using t test (paired or unpaired). On the other hand, if normality is violated, then non-parametric tests like Kruskal–Wallis test by ranks or one-way ANOVA (analysis of variance) can be applied to test whether samples originate from the same distribution. One-way ANOVA is very flexible in the sense that it can work with two or more independent samples, and sample sizes need not be equal. This article emphasizes the fact that marketing strategies of today must take care of predicted consumer inclination, at least in the near future. Prediction of future sales records of competing products can be obtained using many techniques available in the literature, like linear regression, auto-regressive moving average (ARMA) model etc. All these predictions come up with a certain percentage of error. Therefore, it is wise to fuzzify them by dividing into ranges, before comparison. Here, a novel fuzzy logic–based technique is proposed that compares predicted sales records of competing products and accordingly finds out which one is the best.

scholarly journals Forecasting and Modelling of Solar Radiation for Photovoltaic (PV) Systems

2021 ◽  
Author(s):  
Ines Sansa ◽  
Najiba Mrabet Bellaaj
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
Cloud Cover ◽  
Moving Average ◽  
Arma Model ◽  
Weather Conditions ◽  
Pv Systems ◽  
Auto Regressive ◽  
Micro Grid ◽  
One Year

Solar radiation is characterized by its fluctuation because it depends to different factors such as the day hour, the speed wind, the cloud cover and some other weather conditions. Certainly, this fluctuation can affect the PV power production and then its integration on the electrical micro grid. An accurate forecasting of solar radiation is so important to avoid these problems. In this chapter, the solar radiation is treated as time series and it is predicted using the Auto Regressive and Moving Average (ARMA) model. Based on the solar radiation forecasting results, the photovoltaic (PV) power is then forecasted. The choice of ARMA model has been carried out in order to exploit its own strength. This model is characterized by its flexibility and its ability to extract the useful statistical properties, for time series predictions, it is among the most used models. In this work, ARMA model is used to forecast the solar radiation one year in advance considering the weekly radiation averages. Simulation results have proven the effectiveness of ARMA model to forecast the small solar radiation fluctuations.

scholarly journals Union Discourse and Perceived Violation of Contract

2008 ◽  
Vol 63 (3) ◽  
pp. 479-501 ◽  
Author(s):  
Dominique Peyrat-Guillard
Keyword(s):  
Social Contract ◽  
Psychological Contract ◽  
Emotional Reactions ◽  
Senior Management ◽  
The Other ◽  
Breach Of Contract ◽  
Social Contracts ◽  
First Case ◽  
The Social

This article proposes a study of the violation of contract process through a case study. The study is based on a discourse of the union, SUD Michelin, which is contrasted both with those of another union, the CFE-CGC Michelin and of the senior management of the corporation. The results highlight the possibility of applying Morrison and Robinson’s (1997) Psychological Contract Violation model at the social contract level. The emotional reactions appearing in the literature, which are associated with contract violations, can be seen in the union discourse of the SUD. The other union does not perceive any breach of contract. These differences may be attributed to the very nature of social contracts—relational in the first case, and more balanced in the second.

Method for Determining System Eigenvalues From FRF for Noise Contaminated Subsystems

1993 ◽  
Author(s):  
H. C. Chen ◽  
Eric K. Lee ◽  
Y. G. Tsuei
Keyword(s):  
Frequency Response Function ◽  
Numerical Study ◽  
Moving Average ◽  
Arma Model ◽  
Frequency Range ◽  
Laplace Domain ◽  
Force Method ◽  
Modal Force ◽  
Auto Regressive ◽  
Inverse Operation

Abstract A method for determining the eigenvalues of a synthesized system from the Frequency Response Function (FRF) for noise contaminated subsystems is presented. This method first uses matrix Auto-Regressive Moving-Average (ARMA) model in the Laplace domain to describe each subsystem. Then a modal force method by ARMA model can be established. Only the FRF at the connecting joints is needed in the analysis to form a matrix named Modal Force Matrix. From this matrix, both synthesized system modes and substructure modes can be extracted simultaneously. Since the inverse operation is not required to form Modal Force Matrix, the computation is reduced drastically. The eigensolution of the system in any frequency range can be determined independently. Numerical study suggests that good results can be achieved by this method.

ADAPTIVE, FREQUENCY DOMAIN, 2-D MODELING USING SPATIOTEMPORAL SIGNALS

1996 ◽  
Vol 06 (04) ◽  
pp. 351-358
Author(s):  
WASFY B. MIKHAEL ◽  
HAOPING YU
Keyword(s):  
Frequency Domain ◽  
Moving Average ◽  
System Modeling ◽  
Arma Model ◽  
Model Parameters ◽  
Two Dimensional ◽  
Convergence Factor ◽  
Arma Process ◽  
Auto Regressive ◽  
Unknown System

In this paper, an adaptive, frequency domain, steepest descent algorithm for two-dimensional (2-D) system modeling is presented. Based on the equation error model, the algorithm, which characterizes the 2-D spatially linear and invariant unknown system by a 2-D auto-regressive, moving-average (ARMA) process, is derived and implemented in the 3-D spatiotemporal domain. At each iteration, corresponding to a given pair of input and output 2-D signals, the algorithm is formulated to minimize the error-function’s energy in the frequency domain by adjusting the 2-D ARMA model parameters. A signal dependent, optimal convergence factor, referred to as the homogeneous convergence factor, is developed. It is the same for all the coefficients but is updated once per iteration. The resulting algorithm is called the Two-Dimensional, Frequency Domain, with Homogeneous µ*, Adaptive Algorithm (2D-FD-HAA). In addition, the algorithm is implemented using the 2-D Fast Fourier Transform (FFT) to enhance the computational efficiency. Computer simulations demonstrate the algorithm’s excellent adaptation accuracy and convergence speed. For illustration, the proposed algorithm is successfully applied to modeling a time varying 2-D system.

The Application of EMD and ARMA Bi-Cepstrum Fault Diagnosis Method in Gearbox of Overhead Traveling Crane

2011 ◽  
Vol 308-310 ◽  
pp. 88-91
Author(s):  
Hong Bo Xu ◽  
Guo Hua Chen ◽  
Xin Hua Wang ◽  
Jun Liang
Keyword(s):  
Fault Diagnosis ◽  
Evaluation Method ◽  
Moving Average ◽  
Arma Model ◽  
Time Varying ◽  
Stability Evaluation ◽  
Test Results ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Auto Regressive

For the time varying of signals, empirical mode decomposition (EMD) is occupied to modulate signals; auto-regressive moving average (ARMA) of higher accuracy is used to establish model for the signal principal components; then parametric bi-cepstrum estimation is implemented and fault feature is extracted. The test results about gearbox of overhead traveling crane indicate: the feature quefrency can be obtained through method of EMD and ARMA model parametric bi-cepstrum estimation.It is a kind of effective fault diagnosis and stability evaluation method.

scholarly journals Why do Logging Firms Reject Environmental Labeling in Central Africa? A Case Study of Forest Certification in Cameroon

2020 ◽  
Vol 6 (16) ◽  
pp. 59-79
Author(s):  
Isaac Bernard NDOUMBE BEROCK ◽  
◽  
Neba Cletus YAH ◽  
Symphorien ONGOLO ◽  
◽  
...  
Keyword(s):  
Central Africa ◽  
The Other ◽  
Forest Certification ◽  
Intermediate Case ◽  
Environmental Labeling ◽  
First Case ◽  
Negative Case ◽  
Logging Industry ◽  
Empirical Case Studies

This article aims to understand why extractive firms in the industrial logging industry in central Africa are reluctant to certify or label their activities. The methodology is based on three empirical case studies of logging companies in Cameroon: one opposed to certification and labeling (the model), the other is in the process of being certified (intermediate case) and the last is certified (negative case). The preferred option followed by this study was to avoid the copying of the first case by prospecting an intermediate case. The "negative" case permitted the model to be saturated. The comparative analysis of data collected highlighted some key obstacles to the commitment to environmental labeling: corruption, low turnover, high certification cost and the source of capital.

The limitations and usefulness of streamflow generation methods: a case study

1980 ◽  
Vol 7 (1) ◽  
pp. 185-191
Author(s):  
W. J. Stolte
Keyword(s):  
Probabilistic Models ◽  
Moving Average ◽  
Arma Model ◽  
Physical World ◽  
Model Complexity ◽  
Autoregressive Moving Average ◽  
Streamflow Generation ◽  
Recorded Data ◽  
Hydrologic Design

Probabilistic models have become important hydrologic tools. However, increasing model complexity makes the connections between the model and the physical world more and more vague. This can lead to a de-emphasis of engineering judgment, since model validity is easily assumed when even partial verification must await future occurrences. A simple autoregressive model was used to generate stochastic flow sequences for the dam and reservoir being constructed on the Red Deer River in Alberta. The results from this model were compared with those obtained from a more complex autoregressive moving average (ARMA) model. Both models have similar deficiencies. It is concluded that since stochastic generation can never represent future conditions with certainty, the common practice of basing the hydrologic design of reservoirs on actually recorded data is usually the most valid procedure. However, stochastic streamflow generation can be used to give valuable probabilities of reservoir storage failure.

scholarly journals P017: Does a busy day predict another busy day? A time-series analysis of multi-centre emergency department volumes

CJEM ◽  
2016 ◽  
Vol 18 (S1) ◽  
pp. S83-S84 ◽  
Author(s):  
M.B. Butler ◽  
H. Gu ◽  
T. Kenney ◽  
S.G. Campbell
Keyword(s):  
Time Series ◽  
Health Informatics ◽  
Moving Average ◽  
Lunar Cycle ◽  
Model Specification ◽  
True Value ◽  
Auto Correlation ◽  
Seasonal Model ◽  
Time Series Approach ◽  
Auto Regressive

Introduction: Variations of patient volumes in the ED according to days of the week and month of the year are well-established. Anecdotally, ED volumes follow ‘waves’ that correlate with previous days. Time-series models have traditionally been used in econometrics to develop financial models, but have been adapted in other fields, such as health informatics. This study uses a time-series approach to assess whether these impressions are valid. Methods: The daily volume of patients presenting to four emergency departments (ED) at the Nova Scotia Health Authority from Jan 2010 to May 2015 were analyzed to assess for the effect of previous volumes on future volumes. Parameters were selected using the auto-correlation (ACF) and partial auto-correlation functions (PACF) for a Seasonal Auto-regressive Integrated Moving Average (SARIMA) model. The Box-Jenkins statistic was assessed for model suitability. To assess for accuracy, a forecast of the model was evaluated with a year of volumes set aside for testing. Results: The EDs saw an average of 365.1 patients per day, with a minimum of 188 patients and a maximum of 479. The increasing trend in volumes consistent with the increasing number of ED presentations nation-wide was detrended using linear regression. There was a significant correlation in ACF with the previous day (ρ1 = 0.297). A seasonal, periodic trend was seen weekly. Significant correlations occurred annually (ρ365 = 0.279) and at 29 days (ρ29 = 0.339), consistent with the lunar cycle. A seasonal model was postulated incorporating an auto-regressive (AR) coefficient, and a moving average (MA) coefficient for the previous day’s volume. An AR and MA seasonal coefficient were each incorporated using the weekly period. When using the model on the test data, the model predicted 4 more patient presentations on average than the true value, with 90% of the values within 37 presentations of the true volume. The Box-Jenkins statistic was non-significant, indicating no problems with model specification. Conclusion: The volume of patients presenting to an ED system is correlated with that of the previous day. A weekly seasonal variation was confirmed. Auto-correlations also occur annually and possibly associated with the lunar cycle. Previous ED volumes may be useful in forecasting patient volumes. The time-series approach may discover further ways to predict ED volumes.

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