scholarly journals Technical note: A procedure to clean, decompose and aggregate time series

2021 ◽  
Author(s):  
François Ritter
Keyword(s):  
Time Series ◽  
Earth Science ◽  
Technical Note ◽  
Screening Methods ◽  
Term Trend ◽  
Univariate Time Series ◽  
Processing Procedure ◽  
Long Term Trend ◽  
Analysis Of Time Series

Abstract. Errors, gaps and outliers complicate and sometimes invalidate the analysis of time series. While most fields have developed their own strategy to clean the raw data, no generic procedure has been promoted to standardize the pre-processing. This lack of harmonization makes the inter-comparison of studies difficult, and leads to screening methods that are usually ambiguous or case-specific. This study provides a generic pre-processing procedure (called past, implemented in R) dedicated to any univariate time series. Past is based on data binning and decomposes the time series into a long-term trend and a cyclic component (quantified by a new metric, the Stacked Cycles Index) to finally aggregate the data. Outliers are flagged with an enhanced Boxplot rule called Logbox. Three different Earth Science datasets (contaminated with gaps and outliers) are successfully cleaned and aggregated with past. This illustrates the robustness of this procedure that can be valuable to any discipline.

Modelling of overflow emissions in flanders

1998 ◽  
Vol 38 (10) ◽  
pp. 41-48 ◽  
Author(s):  
G. Vaes ◽  
J. Berlamont
Keyword(s):  
Time Series ◽  
Storm Events ◽  
Short Time Series ◽  
Hydrodynamic Simulations ◽  
Sewer Systems ◽  
Linear Behaviour ◽  
Long Time ◽  
Short Time ◽  
Different Characteristics

Ideally, for emission calculations long term hydrodynamic simulations should be performed, but this requires long calculation times. Simplifications are consequently necessary. Due to the non-linear behaviour of sewer systems, hydrodynamic simulations using single storm events often will not lead to a good probability estimation of the overflow emissions. Simplified models using long time simulations give better results if they are well calibrated. To increase the accuracy hydrodynamic simulations with short time series can be used. The short time series are selected from the long time historical rainfall series using a simplified model. To test the accuracy of these three methods, hydrodynamic long term simulations were performed for several (small) sewer systems with different characteristics to compare with.

scholarly journals Analysis of Segmented Sea level Time Series

2020 ◽  
Vol 10 (2) ◽  
pp. 625
Author(s):  
Alberto Boretti
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Indian Ocean ◽  
Break Point ◽  
Tide Gauges ◽  
Sea Levels ◽  
Term Trend ◽  
The Indian Ocean ◽  
Long Term Trend

Records of measurements of sea levels from tide gauges are often “segmented”, i.e., obtained by composing segments originating from the same or different instruments, in the same or different locations, or suffering from other biases that prevent the coupling. A technique is proposed, based on data mining, the application of break-point alignment techniques, and similarity with other segmented and non-segmented records for the same water basin, to quality flag the segmented records. This prevents the inference of incorrect trends for the rate of rise and the acceleration of the sea levels for these segmented records. The technique is applied to the four long-term trend tide gauges of the Indian Ocean, Aden, Karachi, Mumbai, and Fremantle, with three of them segmented.

scholarly journals Shifts between gelatinous and crustacean plankton in a coastal upwelling region

2013 ◽  
Vol 70 (5) ◽  
pp. 934-942 ◽  
Author(s):  
Antonio Bode ◽  
Maria Teresa Álvarez-Ossorio ◽  
Ana Miranda ◽  
Manuel Ruiz-Villarreal
Keyword(s):  
Time Series ◽  
Coastal Upwelling ◽  
Climatic Variability ◽  
Nw Spain ◽  
Term Trend ◽  
Local Factors ◽  
Crustacean Plankton ◽  
Main Component ◽  
Long Term Trend

Abstract Bode, A., Álvarez-Ossorio M. T., Miranda, A., and Ruiz-Villarreal, M. 2013. Shifts between gelatinous and crustacean plankton in a coastal upwelling region. – ICES Journal of Marine Science, 70: 934–942. Variability in the dominance of copepods vs. gelatinous plankton was analysed using monthly time-series covering the last 55 years and related to changes in climatic, oceanographic, and fishery conditions in the upwelling region of Galicia (NW Spain). Seasonality was generally the main component of variability in all groups, both along the coast and in the nearby ocean, but no common long-term trend was found. Coastal copepods increased since the early 1990s, and gelatinous plankton increased in the ocean during the 1980s. Different trends were found for gelatinous plankton in two coastal sites, characterized by increases in either medusae or tunicates. In all series, multiyear periods of relative dominance of gelatinous vs. copepod plankton were evident. In general, copepod periods were observed in positive phases of the main modes of regional climatic variability. Conversely, gelatinous periods occurred during negative climatic phases. However, the low correlations between gelatinous plankton and climatic, oceanographic, or fishery variables suggest that local factors play a major role in their proliferations.

Trends and Cycles in Daily Newspaper Circulation

1950 ◽  
Vol 27 (3) ◽  
pp. 282-287 ◽  
Author(s):  
John Scott Davenport
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
The State ◽  
State University ◽  
Daily Newspaper ◽  
University Of Iowa ◽  
Term Trend ◽  
Industrial Management ◽  
Long Term Trend

Although the long-term trend in newspaper circulation is definitely upward, a Time Series analysis indicates that it is now on the downswing of a cycle. Mr. Davenport is completing his Ph.D. in Industrial Management at the State University of Iowa, where he is assistant to the director of the School of Journalism.

scholarly journals Long-term Trend in Mean Density of Antarctic Krill (Euphausia superba) Uncertain

2021 ◽  
pp. 27-43
Author(s):  
Steven G. Candy
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Antarctic Krill ◽  
Euphausia Superba ◽  
Trend Prediction ◽  
Southwest Atlantic ◽  
General Hypothesis ◽  
Term Trend ◽  
Long Term Trend

Two recent attempts to model the long-term trend in mean density of Antarctic krill in the southwestern sector of the Atlantic using the KRILLBASE dataset using different statistical methods as well as inclusion versus exclusion of data from “non-scientific” nets have resulted in disparate conclusions. The approach that used a linear mixed model (LMM) fitted to the log of mean density, after standardisation was applied to individual net hauls and with means calculated for 12 spatial strata by years between 1976 and 2016, gave a highly statistically significant linear “regional” decline north of 60oS and, to a lesser degree, south of this latitude. The alternative approach that used a ”hurdle” model fitted to the individual net haul data, excluded regional stratification, and excluded non-scientific nets failed to detect an overall significant decline. The method of modelling log transformed means was reappraised and corrected by applying a meta-analytic LMM approach. Additionally, nonlinear smooths in year by region and a smooth in mean “climatological temperature” were included in the LMM. This model showed on average a mostly consistent decline north of 60oS, however, neither trend was significantly different from a no-trend prediction with the trend north of 60oS highly uncertain. Uncertainty of predictions resulted in only weak power to detect a substantial decline of the order of 70% between 1985 and 2005. These model-based inferences neither strongly support nor reject a general hypothesis that there has been a dramatic decline in density of Antarctic krill in the Southwest Atlantic over this period.

scholarly journals Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants Compared to Full-Length Long Signals

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1620
Author(s):  
Airton Borin ◽  
Anne Humeau-Heurtier ◽  
Luiz Virgílio Silva ◽  
Luiz Murta
Keyword(s):  
Time Series ◽  
Multiscale Entropy ◽  
Short Term ◽  
Series Length ◽  
Short Time Series ◽  
Fuzzy Algorithms ◽  
Computing Performance ◽  
Similar Accuracy ◽  
Short Time

Multiscale entropy (MSE) analysis is a fundamental approach to access the complexity of a time series by estimating its information creation over a range of temporal scales. However, MSE may not be accurate or valid for short time series. This is why previous studies applied different kinds of algorithm derivations to short-term time series. However, no study has systematically analyzed and compared their reliabilities. This study compares the MSE algorithm variations adapted to short time series on both human and rat heart rate variability (HRV) time series using long-term MSE as reference. The most used variations of MSE are studied: composite MSE (CMSE), refined composite MSE (RCMSE), modified MSE (MMSE), and their fuzzy versions. We also analyze the errors in MSE estimations for a range of incorporated fuzzy exponents. The results show that fuzzy MSE versions—as a function of time series length—present minimal errors compared to the non-fuzzy algorithms. The traditional multiscale entropy algorithm with fuzzy counting (MFE) has similar accuracy to alternative algorithms with better computing performance. For the best accuracy, the findings suggest different fuzzy exponents according to the time series length.

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