scholarly journals Forecasting Shrimp and Fish Catch in Chilika Lake over Time Series Analysis

2019 ◽  
Author(s):  
Rohan Kumar Raman ◽  
Basanta Kumar Das
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Chilika Lake ◽  
Series Analysis ◽  
Fish Catch ◽  
Over Time

scholarly journals Association of flavoured cigar regulations with wholesale tobacco volumes in Canada: an interrupted time series analysis

2018 ◽  
Vol 28 (4) ◽  
pp. 457-461 ◽  
Author(s):  
Michael O Chaiton ◽  
Robert Schwartz ◽  
Gabrielle Tremblay ◽  
Robert Nugent
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Time Series Analysis ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Level Change ◽  
Series Analysis ◽  
Tobacco Sales ◽  
Health Canada ◽  
Over Time

IntroductionThis study examines the association of Federal Canadian regulations passed in 2009 addressing flavours (excluding menthol) in small cigars with changes in cigar sales.MethodsQuarterly wholesale unit data as reported to Health Canada from 2001 through 2016 were analysed using interrupted time series analysis. Changes in sales of cigars with and without flavour descriptors were estimated. Analyses were seasonally adjusted. Changes in the flavour types were assessed over time.ResultsThe Federal flavour regulations were associated with a reduction in the sales of flavoured cigars by 59 million units (95% CI −86.0 to −32.4). Increases in sales of cigars with descriptors other than flavours (eg, colour or other ambiguous terms) were observed (9.6 million increase (95% CI −1.3 to 20.5), but the overall level (decline of 49.6 million units (95% CI −73.5 to −25.8) and trend of sales of cigars (6.9 million units per quarter (95% CI −8.1 to −5.7)) declined following the ban. Sensitivity analysis showed that there was no substantial difference in effect over time comparing Ontario and British Columbia, suggesting that other provincial tobacco control legislation was not associated with the changes in levels. Analyses suggested that the level change was sensitive to the specification of the date.ConclusionThis study demonstrates that flavour regulations have the potential to substantially impact tobacco sales. However, exemptions for certain flavours and product types may have reduced the effectiveness of the ban, indicating the need for comprehensive, well-designed regulations.

Time Series

2021 ◽  
pp. 288-291
Author(s):  
Jean-Frédéric Morin ◽  
Christian Olsson ◽  
Ece Özlem Atikcan
Keyword(s):  
Time Series ◽  
Social Change ◽  
Time Series Analysis ◽  
Longitudinal Analysis ◽  
Analysis Time ◽  
Social Phenomena ◽  
Series Analysis ◽  
Level Of Analysis ◽  
Patterns Of Recurrence ◽  
Over Time

This chapter focuses on time series analysis, a statistical method of longitudinal analysis which is suitable if researchers are interested in the temporality of social phenomena and want to analyse social change and patterns of recurrence over time. In contrast to other statistical methods of longitudinal analysis, time series analysis can be applied even if researchers have only a few cases (maybe even only one) and only a few (maybe even only one) variables. Time series can be built for any level of analysis, as cases can be persons, but are usually organizations or countries. In order to build a time series, the variables need to have been measured several times over a given period, and for each measurement one needs to know the measurement date. There are different goals when doing time series analysis, which can be used in descriptive, explanatory, and interpretive approaches.

Time-Series Analysis of Suicidal and Undetermined Deaths

1997 ◽  
Vol 85 (3_suppl) ◽  
pp. 1242-1242 ◽  
Author(s):  
David Lester
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Suicide Rate ◽  
Death Rate ◽  
Death Rates ◽  
Negatively Associated ◽  
Fair Proportion ◽  
Series Analysis ◽  
The Usa ◽  
Over Time

The suicide rate and the death rate for undetermined causes were negatively associated over time from 1968 to 1990 in the USA, suggesting that these undetermined deaths may include a fair proportion of suicides. In contrast, there was no association between suicide and undetermined death rates over the states in 1980.

Toward an understanding of team performance and team cohesion over time through the lens of time series analysis

2003 ◽  
Author(s):  
Robert M. McIntyre ◽  
Kari Strobel ◽  
Hope Hanner ◽  
Amy Cunningham ◽  
Lara Tedrow
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Team Performance ◽  
Team Cohesion ◽  
Series Analysis ◽  
Over Time

scholarly journals The Validity of the Enns and Koch, and Berry et al. Measures of State Policy Mood: Continuing the Debate

2021 ◽  
pp. 1-12
Author(s):  
William D. Berry ◽  
Richard C. Fording ◽  
Russell L. Hanson ◽  
Justin K. Crofoot
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Empirical Evidence ◽  
State Policy ◽  
Face Validity ◽  
The South ◽  
Cross Sectional ◽  
Series Analysis ◽  
Statistical Results ◽  
Over Time

Abstract Enns and Koch question the validity of the Berry, Ringquist, Fording, and Hanson measure of state policy mood and defend the validity of the Enns and Koch measure on two grounds. First, they claim policy mood has become more conservative in the South over time; we present empirical evidence to the contrary: policy mood became more liberal in the South between 1980 and 2010. Second, Enns and Koch argue that an indicator’s lack of face validity in cross-sectional comparisons is irrelevant when judging the measure’s suitability in the most common form of pooled cross-sectional time-series analysis. We show their argument is logically flawed, except under highly improbable circumstances. We also demonstrate, by replicating several published studies, that statistical results about the effect of state policy mood can vary dramatically depending on which of the two mood measures is used, making clear that a researcher’s measurement choice can be highly consequential.

Time Series Analysis and Structural Change Detection

2010 ◽  
pp. 377-395
Author(s):  
Kwok Pan Pang
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Time Series Analysis ◽  
Change Detection ◽  
Structural Breaks ◽  
Time Series Data ◽  
Series Data ◽  
Series Analysis ◽  
Us Economy ◽  
Over Time

Most research on time series analysis and forecasting is normally based on the assumption of no structural change, which implies that the mean and the variance of the parameter in the time series model are constant over time. However, when structural change occurs in the data, the time series analysis methods based on the assumption of no structural change will no longer be appropriate; and thus there emerges another approach to solving the problem of structural change. Almost all time series analysis or forecasting methods always assume that the structure is consistent and stable over time, and all available data will be used for the time series prediction and analysis. When any structural change occurs in the middle of time series data, any analysis result and forecasting drawn from full data set will be misleading. Structural change is quite common in the real world. In the study of a very large set of macroeconomic time series that represent the ‘fundamentals’ of the US economy, Stock and Watson (1996) has found evidence of structural instability in the majority of the series. Besides, ignoring structural change reduces the prediction accuracy. Persaran and Timmermann (2003), Hansen (2001) and Clement and Hendry (1998, 1999) showed that structural change is pervasive in time series data, ignoring structural breaks which often occur in time series significantly reduces the accuracy of the forecast, and results in misleading or wrong conclusions. This chapter mainly focuses on introducing the most common time series methods. The author highlights the problems when applying to most real situations with structural changes, briefly introduce some existing structural change methods, and demonstrate how to apply structural change detection in time series decomposition.

A Time-Series Analysis of War and Levels of Interpersonal Violence in an English Military Town, 1700–1781

2007 ◽  
Vol 31 (4) ◽  
pp. 575-602 ◽  
Author(s):  
Jessica Warner ◽  
Gerhard Gmel ◽  
Kathryn Graham ◽  
Bonnie Erickson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Interpersonal Violence ◽  
Significant Variation ◽  
Physical Violence ◽  
Modest Increase ◽  
Series Analysis ◽  
Participation Of Women ◽  
Time Series Analyses ◽  
Over Time

More than 7,000 assaults were reported to the magistrates of Portsmouth, England, between 1700 and 1781. Time-series analyses were run to see (1) what effects, if any, war had on levels of aggression and (2) whether overall levels of aggression decreased over time. Aggression was measured in two ways: (1) the extent to which assailants ganged up on adversaries and (2) levels of violence in individual confrontations (whether a weapon was used, and if so, what type; whether assailants refrained from using a weapon; and whether they stopped short of physical violence and instead merely insulted or threatened their enemies). Neither measure showed a significant variation over time. The participation of women in brawls decreased, but the aggressiveness of those who continued to brawl actually increased. Complaints about insults and threats declined, while complaints of a more serious nature showed a modest increase, reflecting, among other things, the emergence of new definitions of actionable behavior.

Toward an Understanding of Team Performance and Team Cohesion Over Time Through the Lens of Time Series Analysis

2003 ◽  
Author(s):  
Robert M. McIntyre ◽  
Kari Strobel ◽  
Hope Hanner ◽  
Amy Cunningham ◽  
Lara Tedrow
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Team Performance ◽  
Team Cohesion ◽  
Series Analysis ◽  
Over Time
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