scholarly journals Characterization of Export Regimes in Concentration–Discharge Plots via an Advanced Time-Series Model and Event-Based Sampling Strategies

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1723
Author(s):  
Ana Gonzalez-Nicolas ◽  
Marc Schwientek ◽  
Michael Sinsbeck ◽  
Wolfgang Nowak
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Time Series Model ◽  
Data Series ◽  
Additional Parameter ◽  
Sampling Strategies ◽  
Discharge Rates ◽  
Regression Approach ◽  
Event Based ◽  
Stochastic Time Series

Currently, the export regime of a catchment is often characterized by the relationship between compound concentration and discharge in the catchment outlet or, more specifically, by the regression slope in log-concentrations versus log-discharge plots. However, the scattered points in these plots usually do not follow a plain linear regression representation because of different processes (e.g., hysteresis effects). This work proposes a simple stochastic time-series model for simulating compound concentrations in a river based on river discharge. Our model has an explicit transition parameter that can morph the model between chemostatic behavior and chemodynamic behavior. As opposed to the typically used linear regression approach, our model has an additional parameter to account for hysteresis by including correlation over time. We demonstrate the advantages of our model using a high-frequency data series of nitrate concentrations collected with in situ analyzers in a catchment in Germany. Furthermore, we identify event-based optimal scheduling rules for sampling strategies. Overall, our results show that (i) our model is much more robust for estimating the export regime than the usually used regression approach, and (ii) sampling strategies based on extreme events (including both high and low discharge rates) are key to reducing the prediction uncertainty of the catchment behavior. Thus, the results of this study can help characterize the export regime of a catchment and manage water pollution in rivers at lower monitoring costs.

scholarly journals Identification of an unstable ARMA equation

2001 ◽  
Vol 7 (1) ◽  
pp. 97-112 ◽  
Author(s):  
Yulia R. Gel ◽  
Vladimir N. Fomin
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Infinite Order ◽  
Time Series Model ◽  
Identification Algorithm ◽  
Data Handling ◽  
Stochastic Time Series ◽  
Stochastic Time ◽  
Strongly Consistent ◽  
Minimal Phase

Usually the coefficients in a stochastic time series model are partially or entirely unknown when the realization of the time series is observed. Sometimes the unknown coefficients can be estimated from the realization with the required accuracy. That will eventually allow optimizing the data handling of the stochastic time series.Here it is shown that the recurrent least-squares (LS) procedure provides strongly consistent estimates for a linear autoregressive (AR) equation of infinite order obtained from a minimal phase regressive (ARMA) equation. The LS identification algorithm is accomplished by the Padé approximation used for the estimation of the unknown ARMA parameters.

Criteria for Recognition of Wheel Skid of Trains Based on Time Series Analysis

2013 ◽  
Vol 440 ◽  
pp. 237-242
Author(s):  
Jun Bin Peng ◽  
Xiao Yi Hu ◽  
Yong Jun Liu
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Characteristic Equation ◽  
Critical Value ◽  
Time Series Model ◽  
Data Series ◽  
Velocity Difference ◽  
Series Analysis ◽  
Greens Function ◽  
Order Determination

Current criteria to judge wheel skid of trains such as velocity difference often cannot recognize wheel skid timely and have no uniform critical value for different trains or railway lines. Aiming at the disadvantages, new criteria based on time series analysis are proposed. With appropriate method of order determination and parameter estimation, AR time series model is established for the data series of velocity difference. Then, Greens function and characteristic equation are constructed with the parameters of the model to determine wheel skid by the convergence state of Greens function or the value of characteristic equations roots. Simulation result shows that the two criteria based on time series model can recognize wheel skid earlier than velocity difference. Moreover, the roots of characteristic equation can also be used as a criterion with a uniform critical value under different application conditions.

scholarly journals THE COMPARISON OF TWO PRECIPITATION PREDICTION METHODS

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 1025-1032
Author(s):  
X. Q. Mo ◽  
G. W. Lan ◽  
Y. L. Du ◽  
Z. X. Chen
Keyword(s):  
Time Series ◽  
Regression Analysis ◽  
Linear Regression ◽  
Time Series Analysis ◽  
Daily Precipitation ◽  
Linear Regression Analysis ◽  
Time Series Model ◽  
Residual Error ◽  
Actual Situation ◽  
Monthly Average

Abstract. Precipitation forecasts play the role in flood control and drought relief. At present, the time series analysis and the linear regression analysis are two of most commonly used methods. The time series analysis is relatively simple as it only requires historical precipitation data. The model of the linear regression analysis can ensure high accuracy for causality analysis and short, medium and long-term prediction. Guilin is the region of the heavy rain center in Guangxi, which frequently suffers serious losses from rainstorms. Selecting a better model to predict precipitation has the important reference significance for improving the accuracy of precipitation weather forecast. In this research, the two methods are used to predict precipitation in Guilin. According to data of the monthly maximum precipitation, monthly average daily precipitation and monthly total precipitation from 2014 to 2016, this paper establishes the time series model and linear regression analysis model to predict precipitation in 2017 and compare the forecast results. The results show that the monthly average daily precipitation model is best with the accuracy of the time series model, and the residual error of predicted precipitation is 3.08 mm, but the change trend of predicted precipitation is not accord with the actual situation. The residual error is only 0.45 mm through using inter-annual linear regression equation to predict the precipitation, but the predicted summer precipitation is quite different from the actual one. The linear equation established by different seasons is used to predict the precipitation with residual error of 3.25 mm, and it is coincident for the predicted precipitation trend with the actual situation. Furthermore, the predictions fitting errors of spring, summer, autumn and winter are all less than 20%, which are within the scope of the specification prediction error.

scholarly journals Analysis of time series by the example of registration of variations in the gravitational field

2021 ◽  
Vol 43 (4) ◽  
pp. 76-90
Author(s):  
R.Z. Burtiev ◽  
Yu.V. Semenova ◽  
V.T. Kiriyak ◽  
E.V. Sidorenko ◽  
S.V. Troyan ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Data ◽  
Time Series Model ◽  
Statistical Dependence ◽  
Data Series ◽  
Time Series Models ◽  
Geophysical Processes ◽  
Gravimetric Data ◽  
Predicted Values

In this work, a time series model is used to study the structure of gravimetric data series to identify patterns in the change in the levels of the series and build its model in order to predict and study the relationships between the levels of gravimetric data. Observations of the activity of geophysical processes showed that the periods of variations in geophysical processes are scattered chaotically on the time axis. According to their schedule, it is impossible to definitely speak about the regularity in the duration of the periods of variations, and in the alternation of periods of seismic calm with a period of high seismic activity. The impetus for this study was the desire to analyze the structure of a number of formal methods to search for statistical patterns in the variations of geophysical parameters over time. Time series models were used to study the dynamics of geophysical events. Forecasting was carried out using the SPSS 20 package and EXCEL 2016. The accuracy of the forecast is indicated by the comparison of the forecast series with the actual data. The predicted values of the gravimetric data are within the confidence intervals. If you start forecasting too early, the forecast may differ from the forecast based on all statistical data. If the data shows seasonal trends, it is recommended to start forecasting from the date before the last point of the statistical data. Spatial and time series models can be used to study the dynamics of geophysical events. A spatial model describes a set of geophysical parameters at a given point in time. A time series is a series of regular observations of a certain parameter at successive points in time or at intervals of time. In this work, the time series model is used: to identify the statistical relationship between the frequency and depth of occurrence of earthquakes, as well as to identify the statistical dependence of these data on gravimetric variations; determination of patterns in the change in the levels of the series and the construction of its model in order to predict and study the relationships between geophysical phenomena.

scholarly journals Analysis of psychometric data using statistical and machine learning methods

2021 ◽  
Author(s):  
Krishnapriya Subramanian
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Linear Regression ◽  
Well Being ◽  
Time Series Model ◽  
Learning Methods ◽  
Logistics Regression ◽  
Machine Learning Methods ◽  
Psychometric Data ◽  
Simulation Results

The objective of this thesis is to analyse the psychometric data using statistical and machine learning methods. Psychological data are analysed to predict illness and injury of athletes. Regression technique, one of the statistical processes for estimating the relationship among variables is used as basis of this thesis. We apply the linear regression, time series and logistics regression to predict illness and well-being. Our linear regression simulation results are mainly used, to understand the data well. By reviewing the results of linear regression, time series model is developed which predicts sickness one day ahead. The predicted values of this time series model are continuous. However, logistic regression can be used, to provide a probabilistic approach to predict the future levels as a categorical value. Hence we have developed a binomial logistics regression model, when observation variable is the type of dichotomous. Our simulation results show that this prediction model performs well. Our empirical studies also show that our method can act as early warning system for athletes.

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