An analysis of cyclic tidal deposits: statistical time series properties, extraction of earth-moon parameters, and observed intertidal sedimentation

2021 ◽  
Author(s):  
Christopher Lynn Coughenour
Keyword(s):  
Time Series ◽  
Tidal Deposits ◽  
Statistical Time

scholarly journals A Study on The Genesis of "F" Waves of Atrial Flutter in Man Considered as Multiple Statistical Time Series

1980 ◽  
Vol 21 (1) ◽  
pp. 73-83
Author(s):  
Etsu HASHIDA ◽  
Naohiro YOSHITANI ◽  
Takenobu TASAKI
Keyword(s):  
Time Series ◽  
Atrial Flutter ◽  
Statistical Time

Soft Computing Based Statistical Time Series Analysis, Characterization of Chaos Theory, and Theory of Fractals

2013 ◽  
pp. 30-61 ◽  
Author(s):  
Mofazzal H. Khondekar ◽  
Dipendra N. Ghosh ◽  
Koushik Ghosh ◽  
Anup Kumar Bhattacharya
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Time Series Analysis ◽  
Soft Computing ◽  
Chaos Theory ◽  
Filter Design ◽  
Theoretical Perspective ◽  
Series Analysis ◽  
Statistical Time

The present work is an attempt to analyze the various researches already carried out from the theoretical perspective in the field of soft computing based time series analysis, characterization of chaos, and theory of fractals. Emphasis has been given in the analysis on soft computing based study in prediction, data compression, explanatory analysis, signal processing, filter design, tracing chaotic behaviour, and estimation of fractal dimension of time series. The present work is a study as a whole revealing the effectiveness as well as the shortcomings of the various techniques adapted in this regard.

Experimental Verification of the Statistical Time-Series Methods for Diagnosing Wind Turbine Blades Damage

2018 ◽  
Vol 19 (01) ◽  
pp. 1940008 ◽  
Author(s):  
Hesheng Tang ◽  
Suqi Ling ◽  
Chunfeng Wan ◽  
Songtao Xue
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
Experimental Verification ◽  
Turbine Blades ◽  
Ar Model ◽  
Wind Turbine Blades ◽  
Statistical Decision ◽  
Vibration Data ◽  
The Status ◽  
Statistical Time

This paper presents an experimental verification of the statistical time-series methods, which utilize adapted frequency response ratio (FRR), autoregressive (AR) model parameter and AR model residual as performance characteristics, for diagnosing the damage of wind turbine blades. Specifically, the statistical decision-making techniques are used to identify the status patterns from turbine vibration data. For experiments, a small-size, laboratory-used operating wind turbine structure is used. The performance of each method in diagnosing damages simulated by saw cut in three critical positions in the blade are assessed and compared. The experimental results show that these methods yielded a promising damage diagnosis capability in the condition monitoring of wind turbine.

scholarly journals Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses

2019 ◽  
Vol Volume 12 ◽  
pp. 2311-2322 ◽  
Author(s):  
Qiao Liu ◽  
Zhongqi Li ◽  
Ye Ji ◽  
Leonardo Martinez ◽  
Zia Ul Haq ◽  
...  
Keyword(s):  
Time Series ◽  
Pulmonary Tuberculosis ◽  
Jiangsu Province ◽  
Time Series Analyses ◽  
Statistical Time

scholarly journals Impact of Climate Variabilities and Human Activities on Surface Water Extents in Reservoirs of Yongding River Basin, China, from 1985 to 2016 Based on Landsat Observations and Time Series Analysis

2019 ◽  
Vol 11 (5) ◽  
pp. 560 ◽  
Author(s):  
Mingli Wang ◽  
Longjiang Du ◽  
Yinghai Ke ◽  
Maoyi Huang ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Water Resources ◽  
River Basin ◽  
Change Point ◽  
Human Activities ◽  
Reservoir Water ◽  
Change Period ◽  
Statistical Time ◽  
Yongding River

Yongding River is the largest river flowing through Beijing, the capital city of China. In recent years, Yongding River Basin (YDRB) has witnessed increasing human impacts on water resources, posing serious challenges in hydrological and ecological health. In this study, remote sensing techniques and statistical time series approaches for hydrological studies were combined to characterize the dynamics and driving factors of reservoir water extents in YDRB during 1985–2016. First, 107 Landsat 4, 5, 7 and 8 images were used to extract surface water extents in YDRB during 1985–2016 using a combination of water indices and Otsu threshold algorithm. Significant positive correlation was found between water extents and the annual inflow for the two biggest reservoirs, the downstream Guanting and upstream Cetian reservoirs, proving their representativeness of surface water availability in this basin. Then, statistical time series approaches including trend-free pre-whitening Mann-Kendall trend test, Pettit change-point test and double mass curve method, which are frequently used in hydrological studies, were adopted to quantify the trend of reservoir water extents dynamics and the relative contributions of climate variability and human activities. Results showed that the water extents in both reservoirs exhibited significant downward trend with change point occurring in 2001 and 2005 for Guanting and Cetian, respectively. About 74%~75% of the shrinkage during the post-change period can be attributed to human activities, among which GDP, population, electricity power production, raw coal production, steel and crude iron production, value of agriculture output, and urban area were the major human drivers. Hydrological connectivity between the upstream Cetian and downstream Guanting reservoirs declined during the post-change period. Since 2012, water extents in both reservoirs recovered as a result of various governmental water management policies including the South-to-North Water Diversion Project. The methodology presented in this study can be used for analyzing the dynamics and driving mechanism of surface water resources, especially for un-gauged or poorly-gauged watersheds.

What Can We Make of Unsubstantiated Child Abuse Reports? A New Approach

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Michael Malcolm
Keyword(s):  
Time Series ◽  
Child Abuse ◽  
Theoretical Analysis ◽  
General Public ◽  
Child Outcomes ◽  
Empirical Approach ◽  
Previous Literature ◽  
New Approach ◽  
Data Generating Process ◽  
Statistical Time

AbstractOnly about a quarter of child abuse reports are ultimately substantiated, which has caused some concern among policymakers and the general public. But previous literature suggests that unsubstantiated and substantiated reports may not be much different from each other in terms of child outcomes. We present a Bayesian theoretical analysis of the data-generating process underlying maltreatment substantiation, and then take a new empirical approach by examining the statistical time-series relationship between substantiated and unsubstantiated reports. We show that the two series are cointegrated. This suggests that unsubstantiated reports are not mostly malicious or unfounded, but that they emanate from the same signals as verifiable, substantiated abuse.

Sign in / Sign up

Export Citation Format

Share Document