Evaluation of Seasonal Variation Appearing Resistance of Rocks in a Well on a Chirkey HPP Dam With Level Shifts and Interruptions in Observation Range

Descherevsky, A. V.; Idarmachev, Sh. G.; Marchenko, M.N.

doi:10.33580/2541-9684-2019-63-3-52-62

Cited by 2 publications

(2 citation statements)

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“…For estimating the seasonal function S(t), all the remaining signal components should be considered noise. Deshcherevskii et al (2019) give an example of catastrophic distortion of S(t) when it is estimated by the method of epoch superposition from a signal with level shifts and observational gaps.…”

Section: Useful Signal and Noise In The Estimation Of The Seasonal Functionmentioning

confidence: 99%

“…Additional difficulties arise due to the fact that experimental data series normally contain gaps and defects (Gubanov, 2010;Love, 2009;Mandea and Korte, 2011;Deshcherevskii et al, 2016bDeshcherevskii et al, , 2019Gavrilov et al, 2016;Raffinetti et al, 2007;Rosenberg et al, 1994;Scargle et al, 2013). Even if there were no gaps in the process of measurements, they will appear when defects are rejected (Deshcherevskii et al, 2016b;Plotnikov et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Iterative Algorithm for Time Series Decomposition into Trend and Seasonality: Testing Using the Example of CO2 Concentrations in the Atmosphere

Deshcherevskii

Sidorin

2021

Izv. Atmos. Ocean. Phys.

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An iterative algorithm for the decomposition of data series into trend and residual (including the seasonal effect) components is proposed. This algorithm is based on the approaches proposed by the authors in several previous studies and allows unbiased estimates for the trend and seasonal components for data with a strong trend containing different periodic (including seasonal) variations, as well as observational gaps and omissions. The main idea of the algorithm is that both the trend and the seasonal components should be estimated using a signal that is maximally cleaned of any other variations, which are considered a noise. In estimating the trend component, seasonal variation is a noise, and vice versa. The iterative approach allows a priori information to be more completely used in the optimization of models of both trend and seasonal components. The approximation procedure provides maximum flexibility and is fully controllable at all stages of the process. In addition, it allows one to naturally solve the problems in the case of missing observations and defective measurements without filling these dates with artificially simulated values. The algorithm was tested using data on changes in the concentration of CO 2 in the atmosphere at four stations belonging to different latitudinal zones. The choice of these data is explained by the features that complicate the use of other methods, namely, high interannual variability, high-amplitude seasonal variations, and gaps in the series of observed data. This algorithm made it possible to obtain trend estimates (which are of particular importance for studying the characteristics and searching for the causes of global warming) for any time interval, including those that are not multiples of an integer number of years. The rate of increase in the CO 2 content in the atmosphere has also been analyzed. It has been reliably established that in around 2016, the rate of CO 2 accumulation in the atmosphere became stabilized and even tended to decrease. The extent of this stabilization will become clear in the next 2–3 years as new data are available.

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Section: Useful Signal and Noise In The Estimation Of The Seasonal Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative Algorithm for Time Series Decomposition into Trend and Seasonality: Testing Using the Example of CO2 Concentrations in the Atmosphere

Deshcherevskii

Sidorin

2021

Izv. Atmos. Ocean. Phys.

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An Algorithm for Adaptive Assessment in Time Series Seasonal Oscillations and Its Testing Based on the Example of Variations in the CO2 Concentration in the Atmosphere

Deshcherevskii

Sidorin

2022

Izv. Atmos. Ocean. Phys.

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Evaluation of Seasonal Variation Appearing Resistance of Rocks in a Well on a Chirkey HPP Dam With Level Shifts and Interruptions in Observation Range

Cited by 2 publications

References 0 publications

Iterative Algorithm for Time Series Decomposition into Trend and Seasonality: Testing Using the Example of CO2 Concentrations in the Atmosphere

Iterative Algorithm for Time Series Decomposition into Trend and Seasonality: Testing Using the Example of CO2 Concentrations in the Atmosphere

An Algorithm for Adaptive Assessment in Time Series Seasonal Oscillations and Its Testing Based on the Example of Variations in the CO2 Concentration in the Atmosphere

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