31. MODELING OF LONG-TERM TIME SERIES OF WAVE AND WIND — CASE STUDY AT JOETSU COAST

2009 ◽  
Author(s):  
Hajime Mase ◽  
Tomohiro Yasuda ◽  
Nobuhito Mori
Keyword(s):  
Time Series

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.

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.

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.

scholarly journals Data analysis toolkit for long-term, large-scale experiments

2012 ◽  
Vol 76 (8) ◽  
pp. 3355-3364 ◽  
Author(s):  
D. P. Bennett ◽  
R. J. Cuss ◽  
P. J. Vardon ◽  
J. F. Harrington ◽  
R. N. Philp ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Event Detection ◽  
Large Scale ◽  
Scientific Information ◽  
Frequency Component ◽  
Series Component

AbstractA new data analysis toolkit which is suitable for the analysis of large-scale, long-term datasets and the phenomenon/anomalies they represent is described. The toolkit aims to expose and quantify scientific information in a number of forms contained within a time-series based dataset in a quantitative and rigorous manner, reducing the subjectivity of observations made, thereby supporting the scientific observer. The features contained within the toolkit include the ability to handle non-uniform datasets, time-series component determination, frequency component determination, feature/event detection and characterization/parameterization of local behaviours. An application is presented of a case study dataset arising from the 'Lasgit' experiment.

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