Effects of periodization in long-term training on the dynamics of changes in punch endurance of a boxer – case study

2017 ◽  
Vol 2 (8) ◽  
pp. 111-115
Author(s):  
Pavol Hlavačka ◽  
Ľuboslav Šiška ◽  
Jaroslav Broďáni
Keyword(s):  
Time Series ◽  
Statistical Significance ◽  
Cumulative Effect ◽  
Time Shift ◽  
Endurance Test ◽  
Special Training ◽  
Transformation Stage ◽  
Level Of Significance

Introduction. The aim of this work was to monitor the changes a boxer undergoes in the punch endurance test in relation to special training indicators and subsequently, by means of correlation of the time series, to determine the time shift of the delayed cumulative effect in long-term preparation of boxers. Material and methods. The work has an intraindividual basis. By means of the training logs, we recorded the special training indicators and periodization in the training cycles in accordance with the sporting calendar. The athlete under observation carried out a special punch endurance test on the punch bag in regular 4-week intervals, whose duration was identical with the competitive match. The test was issued by the International Boxing Association (AIBA) in the AIBA Coaches Manual (AIBA Coaches Commission, 2011). When correlating the time series, we used the Spearman’s correlation coefficient. The statistical significance of the relationships has been judged at a 20 % level of significance. Results. The average count in the punch endurance test was RTC1 830,17 ± 75,67 punches and RTC2 867 ± 40,36 punches. Statistically significant correlations with training means of speed endurance (SpdE) 1-2 time shifts (2-4 weeks) and sparring (TT S) 1-3 time shifts (2-6 weeks) have been demonstrated. Conclusions. In terms of the dynamics of changes in special punch endurance, the development copied the systematic periodization of training load, the level improved from accumulation, through the intensification up to the transformation stage, where the best test results were achieved before the top events.

Time Series Forecasting in Retail Sales Using LSTM and Prophet

2021 ◽  
pp. 241-262
Author(s):  
Clony Junior ◽  
Pedro Gusmão ◽  
José Moreira ◽  
Ana Maria M. Tome
Keyword(s):  
Time Series ◽  
Data Science ◽  
Short Term Memory ◽  
Special Kind ◽  
Time Series Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Use Of Time

Data science highlights fields of study and research such as time series, which, although widely explored in the past, gain new perspectives in the context of this discipline. This chapter presents two approaches to time series forecasting, long short-term memory (LSTM), a special kind of recurrent neural network (RNN), and Prophet, an open-source library developed by Facebook for time series forecasting. With a focus on developing forecasting processes by data mining or machine learning experts, LSTM uses gating mechanisms to deal with long-term dependencies, reducing the short-term memory effect inherent to the traditional RNN. On the other hand, Prophet encapsulates statistical and computational complexity to allow broad use of time series forecasting, prioritizing the expert's business knowledge through exploration and experimentation. Both approaches were applied to a retail time series. This case study comprises daily and half-hourly forecasts, and the performance of both methods was measured using the standard metrics.

scholarly journals Analysis of global water vapour trends from satellite measurements in the visible spectral range

2007 ◽  
Vol 7 (4) ◽  
pp. 11761-11796 ◽  
Author(s):  
S. Mieruch ◽  
S. Noël ◽  
H. Bovensmann ◽  
J. P. Burrows
Keyword(s):  
Time Series ◽  
Water Vapour ◽  
Statistical Significance ◽  
Visible Spectral Range ◽  
Optical Absorption Spectroscopy ◽  
Data Set ◽  
Factors Affecting ◽  
Scanning Imaging ◽  
Global Water

Abstract. Global water vapour total column amounts have been retrieved from spectral data provided by the Global Ozone Monitoring Experiment (GOME) flying on ERS-2, which was launched in April 1995, and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT launched in March 2002. For this purpose the Air Mass Corrected Differential Optical Absorption Spectroscopy (AMC-DOAS) approach has been used. The combination of the data from both instruments provides us with a long-term global data set spanning more than 11 years with the potential of extension up to 2020 by GOME-2 data, on Metop. Using linear and non-linear methods from time series analysis and standard statistics the trends of H2O contents and their errors have been calculated. In this study, factors affecting the trend such as the length of the time series, the magnitude of the variability of the noise, and the autocorrelation of the noise are investigated. Special emphasis has been placed on the calculation of the statistical significance of the observed trends, which reveal significant local changes of water vapour columns distributed over the whole globe.

Trends in rainfall intensity for stormwater designs in Ontario

2012 ◽  
Vol 3 (1) ◽  
pp. 1-10 ◽  
Author(s):  
B. Vasiljevic ◽  
E. McBean ◽  
B. Gharabaghi
Keyword(s):  
Rainfall Intensity ◽  
Statistical Significance ◽  
Sewer Pipes ◽  
Rainfall Events ◽  
Rain Gauges ◽  
Long Term Trends ◽  
Using Data ◽  
Storm Sewer

The intensities of short-duration rainfall events are fundamental inputs to the design of stormwater management infrastructure. However, since stormwater infrastructure must function as designed for many decades, if there are long-term trends in rainfall intensities, design storms need to be modified. Evidence demonstrates, using data from 13 rain gauges in Ontario, that storm intensities relevant to urban stormwater (5 year) appear to have changed over the last 30 years. The results show, for example, statistical significance at 80% confidence that the 5-year storm has increased, and 85% that the 2-year storm has increased, for the 1 h storm in Waterloo, using partial duration series (PDS) data. The PDS data indicate intensities are increasing at a rate of 1–3% per year. Results show, for example, that a 5-year recurrence storm for PDS for the period 1970–1984 is now very close in magnitude to a 2-year recurrence storm for the period 1985–2003 for Waterloo, Ontario. The implications for a case study demonstrate that 5 out of 12 storm sewer pipes in a subdivision would need to be increased in diameter to obtain the same level of stormwater performance.

Evaluation of phytoplankton quality and toxicity risk based on a long-term time series previous to the implementation of a bivalve farm (Basque coast as a case study)

2017 ◽  
Vol 10 ◽  
pp. 10-19 ◽  
Author(s):  
Oihane Muñiz ◽  
Marta Revilla ◽  
José Germán Rodríguez ◽  
Aitor Laza-Martínez ◽  
Sergio Seoane ◽  
...  
Keyword(s):  
Time Series ◽  
Toxicity Risk ◽  
Basque Coast

Detecting and Correcting Biases in Long-Term Ocean Observatory Time Series: Case Study on Current Directions Estimated From Acoustic Doppler Current Profiler Data

2016 ◽  
Vol 50 (3) ◽  
pp. 109-113
Author(s):  
Michael G. Morley ◽  
Marlene A. Jeffries ◽  
Steven F. Mihály ◽  
Reyna Jenkyns ◽  
Ben R. Biffard
Keyword(s):  
Time Series ◽  
Acoustic Doppler Current Profiler ◽  
Lessons Learned ◽  
Ocean Current ◽  
Data Types ◽  
Current Profiler ◽  
Future Data ◽  
Multiple Deployments

AbstractOcean Networks Canada (ONC) operates the NEPTUNE and VENUS cabled ocean observatories to collect continuous data on physical, chemical, biological, and geological ocean conditions over multiyear time periods. Researchers can download real-time and historical data from a large variety of instruments to study complex earth and ocean processes from their home laboratories. Ensuring that the users are receiving the most accurate data is a high priority at ONC, requiring QAQC (quality assurance and quality control) procedures to be developed for a variety of data types (Abeysirigunawardena et al., 2015). Acquiring long-term time series of oceanographic data from remote locations on the seafloor presents significant challenges from a QAQC perspective. In order to identify and study important scientific events and trends, data consolidated from multiple deployments and instruments need to be self-consistent and free of biases due to changes to instrument configurations, calibrations, metadata, biofouling, or a degradation in instrument performance. As a case study, this paper describes efforts at ONC to identify and correct systematic biases in ocean current directions measured by ADCPs (acoustic Doppler current profilers), as well as the lessons learned to improve future data quality.

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