scholarly journals People Prefer Simpler Content When There Are More Choices: A Time Series Analysis of Lyrical Complexity in Six Decades of American Popular Music

2019 ◽  
Author(s):  
Michael E. W. Varnum ◽  
Jaimie Krems ◽  
Colin Morris ◽  
Igor Grossmann
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Popular Music ◽  
Real World ◽  
Cultural Transmission ◽  
Popular Songs ◽  
Song Lyrics ◽  
American Popular Music ◽  
Information Landscape ◽  
First Time

Song lyrics are rich in meaning. In recent years, the lyrical content of popular songs has been used as an index of shifting norms, affect, and values at the cultural level. One remarkable, recently-uncovered trend is that successful pop songs have increasingly simple lyrics. Why? We test the idea that increasing lyrical simplicity is linked to a widening array of novel song choices. To test this Cultural Compression Hypothesis (CCH), we examined six decades of popular music (N = 14,661 songs). The number of novel song choices predicted greater lyrical simplicity of successful songs. This relationship was robust, holding when controlling for critical ecological and demographic factors and also when using a variety of approaches to account for the potentially confounding influence of temporal autocorrelation. The present data provide the first time series evidence that real-world cultural transmission may depend on the amount of novel choices in the information landscape.

scholarly journals Why are song lyrics becoming simpler? a time series analysis of lyrical complexity in six decades of American popular music

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244576
Author(s):  
Michael E. W. Varnum ◽  
Jaimie Arona Krems ◽  
Colin Morris ◽  
Alexandra Wormley ◽  
Igor Grossmann
Keyword(s):  
Popular Music ◽  
Cultural Transmission ◽  
Ecological Factors ◽  
The United States ◽  
Popular Songs ◽  
Pathogen Prevalence ◽  
Song Lyrics ◽  
American Popular Music ◽  
Information Landscape ◽  
Do So

Song lyrics are rich in meaning. In recent years, the lyrical content of popular songs has been used as an index of culture’s shifting norms, affect, and values. One particular, newly uncovered, trend is that lyrics of popular songs have become increasingly simple over time. Why might this be? Here, we test the idea that increasing lyrical simplicity is accompanied by a widening array of novel song choices. We do so by using six decades (1958–2016) of popular music in the United States (N = 14,661 songs), controlling for multiple well-studied ecological and cultural factors plausibly linked to shifts in lyrical simplicity (e.g., resource availability, pathogen prevalence, rising individualism). In years when more novel song choices were produced, the average lyrical simplicity of the songs entering U.S. billboard charts was greater. This cross-temporal relationship was robust when controlling for a range of cultural and ecological factors and employing multiverse analyses to control for potentially confounding influence of temporal autocorrelation. Finally, simpler songs entering the charts were more successful, reaching higher chart positions, especially in years when more novel songs were produced. The present results suggest that cultural transmission depends on the amount of novel choices in the information landscape.

TESTING FOR REMAINING AUTOCORRELATION OF THE RESIDUALS IN THE FRAMEWORK OF FUZZY RULE-BASED TIME SERIES MODELLING

2010 ◽  
Vol 18 (04) ◽  
pp. 371-387 ◽  
Author(s):  
JOSÉ LUIS AZNARTE ◽  
MARCELO C. MEDEIROS ◽  
JOSÉ M. BENÍTEZ
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Dependence ◽  
Real World ◽  
Hypothesis Test ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Testing Framework ◽  
Diagnostic Checking ◽  
Time Series Modelling

In time series analysis remaining autocorrelation in the errors of a model implies that it is failing to properly capture the structure of time-dependence of the series under study. This can be used as a diagnostic checking tool and as an indicator of the adequacy of the model. Through the study of the errors of the model in the Lagrange Multiplier testing framework, in this paper we derive (and validate using simulated and real world examples) a hypothesis test which allows us to determine if there is some left autocorrelation in the error series. This represents a new diagnostic checking tool for fuzzy rule-based modelling of time series and is an important step towards statistically sound modelling strategy for fuzzy rule-based models.

scholarly journals First time-resolved CCD photometry and time-series analysis of NSV 13601

2020 ◽  
Vol 20 (3) ◽  
pp. 039
Author(s):  
Virabhadrasinh A. Gohil ◽  
Sachchidanand Prakash Bhatnagar
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Ccd Photometry ◽  
Time Resolved ◽  
Series Analysis ◽  
First Time

scholarly journals Establishment of a terminal prognosis prediction model by applying time series analysis to real-world data

2015 ◽  
Vol 26 ◽  
pp. vii99 ◽  
Author(s):  
Yu Uneno ◽  
Kei Taneishi ◽  
Masashi Kanai ◽  
Akiko Tamon ◽  
Kazuya Okamoto ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Prediction Model ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Prognosis Prediction ◽  
Series Analysis ◽  
Prognosis Prediction Model ◽  
Terminal Prognosis

scholarly journals Time series analysis (orbital cycles) of the uppermost Cenomanian-Lower Turonian sequence on the southern Tethyan margin using foraminifera

2010 ◽  
Vol 61 (2) ◽  
pp. 111-120 ◽  
Author(s):  
Mohamed Soua
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Oceanic Anoxic Event ◽  
Series Analysis ◽  
Orbital Cycles ◽  
Anoxic Event ◽  
Minimum Zone ◽  
First Time ◽  
Shed Light ◽  
Oxygen Minimum

Time series analysis (orbital cycles) of the uppermost Cenomanian-Lower Turonian sequence on the southern Tethyan margin using foraminiferaTime series analysis has been performed for the first time on the Cenomanian-Turonian sequence in Central Tunisia in order to shed light on its Milankovitch-like cyclicity. This analysis was applied to two foraminiferal genera: the biserialHeterohelix, an oxygen-minimum zone (OMZ) dweller, and the triserialGuembelitria, a eutrophic surface dweller. Average sedimentary rates and the duration of the oceanic anoxic event (OAE2) in each studied section were estimated. The fluctuations in abundance of these two opportunistic species can be related mainly to both precessional (ca. 20 kyr) and eccentricity (100 and 400 kyr) cyclicity suggesting that changes in surface water fertility were linked to climate changes in the Milankovitch frequency band.

scholarly journals LAND SUBSIDENCE HAZARD IN IRAN REVEALED BY COUNTRY-SCALE ANALYSIS OF SENTINEL-1 INSAR

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 155-161
Author(s):  
M. Haghshenas Haghighi ◽  
M. Motagh
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Time Series Analysis ◽  
Land Subsidence ◽  
Second Step ◽  
Civil Infrastructure ◽  
Scale Analysis ◽  
Medium Scale ◽  
The Past ◽  
First Time

Abstract. Many areas across Iran are subject to land subsidence, a sign of exceeding stress due to the over-extraction of groundwater during the past decades. This paper uses a huge dataset of Sentinel-1, acquired since 2014 in 66 image frames of 250 × 250 km, to identify and monitor land subsidence across Iran. Using a two-step time series analysis, we first identify subsidence zones at a medium scale of 100 m across the country. For the first time, our results provide a comprehensive nationwide map of subsidence in Iran and recognize its spatial distribution and magnitude. Then, in the second step of analysis, we quantify the deformation time series at the highest possible resolution to study its impact on civil infrastructure. The results spots the hazard posed by land subsidence to different infrastructure. Examples of road and railways affected by land subsidence hazard in Tehran and Mashhad, two of the most populated cities in Iran, are presented in this study.

scholarly journals Comparison of Stochastic and Machine Learning Methods for Multi-Step Ahead Forecasting of Hydrological Processes

2017 ◽  
Author(s):  
Georgia A. Papacharalampous ◽  
Hristos Tyralis ◽  
Demetris Koutsoyiannis
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Stochastic Processes ◽  
Real World ◽  
Large Scale ◽  
Simulation Experiment ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Stationary Stochastic Processes ◽  
First Time

We perform an extensive comparison between 11 stochastic to 9 machine learning methods regarding their multi-step ahead forecasting properties by conducting 12 large-scale computational experiments. Each of these experiments uses 2 000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 110 values and the second time using time series of 310 values. Additionally, we conduct 92 real-world case studies using mean monthly time series of streamflow and particularly focus on one of them to reinforce the findings and highlight important facts. We quantify the performance of the methods using 18 metrics. The results indicate that the machine learning methods do not differ dramatically from the stochastic, while none of the methods under comparison is uniformly better or worse than the rest. However, there are methods that are regularly better or worse than others according to specific metrics.

scholarly journals Health Effects of Climate and Air Pollution in Buenos Aires: A First Time Series Analysis

2012 ◽  
Vol 03 (03) ◽  
pp. 262-271 ◽  
Author(s):  
Rosana Abrutzky ◽  
Laura Dawidowski ◽  
Patricia Matus ◽  
Patricia Romero Lankao
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Time Series Analysis ◽  
Health Effects ◽  
Buenos Aires ◽  
Series Analysis ◽  
First Time

scholarly journals Land subsidence in Beijing from 2017-2018 revealed by Sentinel-1A TOPS time series interferometry

2020 ◽  
Vol 218 ◽  
pp. 04002
Author(s):  
Peilian Ran ◽  
Shaoda Li ◽  
Keren Dai ◽  
Xiaoxia Yang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Land Subsidence ◽  
Subsidence Rate ◽  
Subsidence Area ◽  
Long Time ◽  
Maximum Subsidence ◽  
The Government ◽  
Sbas Insar ◽  
First Time

Beijing is one of the largest cities in China, which has suffered from land subsidence for a long time. According to the study from 2005 to 2017, the maximum subsidence rate of Beijing is more than 10 cm/year. This paper will use Sentinel-1A TOPS data for the first time to reveal the land subsidence of Beijing from 2017 to 2018 by using time series interferometry. SBAS-InSAR technology was used for time series analysis. The annual mean subsidence rate and time series subsidence of Beijing were obtained. The results show that the east of Chaoyang district and the northwest of Tongzhou district were the severe subsidence areas in Beijing, and the subsidence rate is more than 10 cm/year, which indicates that the subsidence area in Beijing is continuous in recent years, and corresponding measures should be taken by the government.

Sign in / Sign up

Export Citation Format

Share Document