Study of Local Ice Loads Measured at Norströmsgund Lighthouse

2017 ◽  
Author(s):  
Petr Zvyagin ◽  
Gesa Ziemer
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Stationary Process ◽  
The Other ◽  
Stationary Time Series ◽  
Distribution Law ◽  
Local Loads ◽  
Simultaneous Record ◽  
Key Factor ◽  
Ice Loads

It is believed that ice loading can be a stationary process at least sometimes during the state of continuous brittle crushing. Confidence in the distribution law, stationarity in time, and autocorrelation function of local ice loads is the key factor for assessment of such loads and their successful simulation. Good understanding of the load process on the level of a single transducer record can be helpful in future analysis and simulation of loads on wider contact areas. In this paper local loads, simultaneously measured by two middle subpanels at the Norströmsgrund lighthouse in March 2001, are studied. Stationary time series of lognormal origin of 50 seconds duration are extracted from both of the subpanel records. From the studied data, stationarity was not observed simultaneously at different subpanels. The correlation of one stationary subpanel record with simultaneous record of the other subpanel found to be weak. A simple function with good fit to the observed autocorrelation curve of stationary load fragments is suggested. The findings are compared with parameters obtained for local loads in previous studies. A transition from autocorrelation function for raw lognormal data to autocorrelation function of logarithmic normal data is performed.

A note on the asymptotic eigenvalues and eigenvectors of the dispersion matrix of a second-order Stationary Process on a d-dimensional Lattice

1986 ◽  
Vol 23 (02) ◽  
pp. 529-535 ◽  
Author(s):  
R. J. Martin
Keyword(s):  
Time Series ◽  
Stationary Process ◽  
Second Order ◽  
Stationary Time Series ◽  
Stationary Processes ◽  
Dispersion Matrix ◽  
Eigenvalues And Eigenvectors ◽  
Dimensional Lattice ◽  
Stationary Time

A sufficiently large finite second-order stationary time series process on a line has approximately the same eigenvalues and eigenvectors of its dispersion matrix as its counterpart on a circle. It is shown here that this result can be extended to second-order stationary processes on a d-dimensional lattice.

Study on Distribution Law and Stationarity of Global Ice Loads Registered in Experiments in Ice Tank

2016 ◽  
Author(s):  
Aleksei Dobrodeev ◽  
Petr Zvyagin ◽  
Kirill Sazonov
Keyword(s):  
Time Series ◽  
Offshore Structures ◽  
Research Center ◽  
Distribution Law ◽  
Size And Shape ◽  
State Research ◽  
Ice Loads ◽  
Process Variance ◽  
The Moment

Being confident in ice loads time series distribution law is extremely important for analysis, as well as for performing simulations. Strong autocorrelation, which usually exist in registered data, obstructs making outcomes on process distribution. Furthermore, at the moment there are only cautious suggestions exist about connection of ice loads distribution law with size and shape of structure or its fragment. In the paper new results about distribution law of ice loads time series, which were registered in experiments with indenters and models of offshore structures in Krylov State Research Center ice tank (St. Petersburg), are presented. Experiments with four thin indenters were taken in consideration. Also results of tests on process’ variance and means constancy for mentioned time series are presented. Such analysis was performed using methods and software, developed in St. Petersburg State Polytechnic University and Krylov State Research Center. As a result, hypotheses on ice loads stationarity are tested. Results are discussed.

A note on the asymptotic eigenvalues and eigenvectors of the dispersion matrix of a second-order Stationary Process on a d-dimensional Lattice

1986 ◽  
Vol 23 (2) ◽  
pp. 529-535 ◽  
Author(s):  
R. J. Martin
Keyword(s):  
Time Series ◽  
Stationary Process ◽  
Second Order ◽  
Stationary Time Series ◽  
Stationary Processes ◽  
Dispersion Matrix ◽  
Eigenvalues And Eigenvectors ◽  
Dimensional Lattice ◽  
Stationary Time

A sufficiently large finite second-order stationary time series process on a line has approximately the same eigenvalues and eigenvectors of its dispersion matrix as its counterpart on a circle. It is shown here that this result can be extended to second-order stationary processes on a d-dimensional lattice.

Simulation of Normal Differentiable Stochastic Process of Ice Loads

2017 ◽  
Author(s):  
Petr Zvyagin
Keyword(s):  
Stochastic Process ◽  
Autocorrelation Function ◽  
Critical Level ◽  
Process Approach ◽  
The Other ◽  
Normal Process ◽  
Expected Number ◽  
Stationary Model ◽  
Tank Experiment ◽  
Ice Loads

Ice loads time series should be treated in the other way than separate independent ice loads observations. The stochastic process approach can provide information about such important characteristic as mean length of signal’s outcome beyond some critical level and expected number of such outcomes. The paper considers global ice loads registered in an ice tank experiment with a cylindrical indenter of 100 mm width. The autocorrelation function is fitted in a manner that the observed load process is differentiable. The study conducted in the paper demonstrates that characteristics, such as the number outcomes beyond some critical level and the time spent off this level, are governed in the same way as parameters of a stationary differentiable normal process. Normal stationary model of ice loads process allows its simulation, if autocorrelation function is given. In the paper, such simulation is performed.

On Autocorrelation of Ice Loads, Measured in Ice Tank Experiments

2016 ◽  
Author(s):  
Petr Zvyagin
Keyword(s):  
Time Series ◽  
Temporal Dynamics ◽  
Sampling Rate ◽  
Research Centre ◽  
Time Interval ◽  
Wide Sense ◽  
Distribution Law ◽  
High Sampling Rate ◽  
Ice Loads ◽  
The Moment

Temporal dynamics of ice loads, which are measured with high sampling rate in experiments in ice tank, usually show strong autocorrelation. At the moment it is lack of study on autocorrelation function (ACF) of ice loads time series. In this paper the stochastic processes approach will be applied to analyze autocorrelation of ice loads. Stochastic models for ice loads, developed earlier by the author, allow determining the distribution law and stationarity (in wide sense) for some of such time series in statistically confident manner. That allows conducting further study of those time series ACF. For analysis and correct sampling rate choice it is important to know the time interval, which separates two statistically independent data points in time series. The algorithm for finding of such interval for time series with normal and lognormal distribution was developed in the paper. That algorithm was applied to find independence distance for global loads records, obtained in experiments with cylindrical models in ice tank of Krylov State Research Centre (St. Petersburg). The independence distance for those time series occurred to be 0,07–0,35 sec. That distance had increased with increasing of indenter diameter. Obtained results are discussed.

scholarly journals A persistence‐based Wold‐type decomposition for stationary time series

10.3982/qe994 ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 203-230 ◽  
Author(s):  
Fulvio Ortu ◽  
Federico Severino ◽  
Andrea Tamoni ◽  
Claudio Tebaldi
Keyword(s):  
Time Series ◽  
Stationary Process ◽  
Financial Time Series ◽  
Variance Decomposition ◽  
Stationary Time Series ◽  
Wold Decomposition ◽  
Process Innovations ◽  
Financial Time ◽  
Stationary Time ◽  
Type Decomposition

This paper shows how to decompose weakly stationary time series into the sum, across time scales, of uncorrelated components associated with different degrees of persistence. In particular, we provide an Extended Wold Decomposition based on an isometric scaling operator that makes averages of process innovations. Thanks to the uncorrelatedness of components, our representation of a time series naturally induces a persistence‐based variance decomposition of any weakly stationary process. We provide two applications to show how the tools developed in this paper can shed new light on the determinants of the variability of economic and financial time series.

The Iranian Drug-related Deaths with Time Series Modeling and Forecasting: 2014–2016

2020 ◽  
Author(s):  
Abbas Alipour ◽  
Omid Kharazmi ◽  
Masoudeh Babakhanian ◽  
Mehran Zarghami ◽  
Ardeshir Khosravi ◽  
...  
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Illicit Drugs ◽  
Arima Model ◽  
Temporal Variations ◽  
Stationary Time Series ◽  
Seasonal Effects ◽  
Model Parameters ◽  
Entire Study ◽  
Death Toll

Abstract Background: Investigating the temporal variations and forecasting the trends in drug-related deaths can help prevent health problems and develop intervention programs. Iran's recent policy is strongly focused on deterring drug use and replacing illicit drugs with legal drugs. This study was conducted to investigate drug-related deaths in 2014-2016 in Iran and forecast the death toll by 2019.Methods: This longitudinal study used a Box-Jenkins time series analysis to forecast referrals with a diagnosis of drug-related death. The number of referrals was extracted from March 2014 to March 2017 by month. After using appropriate data transformation methods to create a stationary time series and carrying out a more accurate examination of the stationary assumption by the Dickey-Fuller test, the ARIMA model parameters were determined using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) graphs. Comparing the Akaike statistics of the proposed models led to the selection of ARIMA (0,1,2) as the best model for fit to the data. In the final stage, the number of deaths was forecast in Iran until 2019 using the ARIMA (0,1,2) model. The final extracted data were analyzed in R software, Minitab and SPSS-23.Results: The death toll in Iran during the entire study period of three years was 8883 according to the Iranian Ministry of Health and the Legal Medicine Organization. The number of deaths varied by year and was 2840 in 2014, 2810 in 2015 and 3233 in 2016. According to the time-series findings, data on the number of deaths showed an increasing trend and was not under any regular seasonal effects. In addition, the mean number of deaths per month in Iran until 2019 was forecast as 245.8.Conclusion: This study shows that the trend of drug-related deaths rose during the study period, and the modeling process for their forecasting suggests that this trend shall continue until 2019 if proper interventions are not instituted.

Producers' Response to Changes in Prices and Marketing Policies: A Case Study of Sugarcane and Paddy in Eastern U.P. by S. C. Gupta and A. Majid. New York: Asia Publishing House, 1965.

1968 ◽  
Vol 8 (2) ◽  
pp. 308-309
Author(s):  
Mohammad Irshad Khan
Keyword(s):  
New York ◽  
Time Series ◽  
Time Series Data ◽  
Publishing House ◽  
Economic Incentives ◽  
The Other ◽  
Series Data ◽  
Poor Countries ◽  
Northern India

It is alleged that the agricultural output in poor countries responds very little to movements in prices and costs because of subsistence-oriented produc¬tion and self-produced inputs. The work of Gupta and Majid is concerned with the empirical verification of the responsiveness of farmers to prices and marketing policies in a backward region. The authors' analysis of the respon¬siveness of farmers to economic incentives is based on two sets of data (concern¬ing sugarcane, cash crop, and paddy, subsistence crop) collected from the district of Deoria in Eastern U.P. (Utter Pradesh) a chronically foodgrain deficit region in northern India. In one set, they have aggregate time-series data at district level and, in the other, they have obtained data from a survey of five villages selected from 170 villages around Padrauna town in Deoria.

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