The wealth→life history→innovation account of the Industrial Revolution is largely inconsistent with empirical time series data

2019 ◽  
Vol 42 ◽  
Author(s):  
Michael E. W. Varnum ◽  
Igor Grossmann
Keyword(s):  
Time Series ◽  
Life History ◽  
Industrial Revolution ◽  
Time Series Data ◽  
Living Standards ◽  
Life History Strategies ◽  
Series Data ◽  
Bidirectional Relationship ◽  
Dramatic Rise ◽  
Time Lagged

Abstract Baumard proposes a model to explain the dramatic rise in innovation that occurred during the Industrial Revolution, whereby rising living standards led to slower life history strategies, which, in turn, fostered innovation. We test his model explicitly using time series data, finding limited support for these proposed linkages. Instead, we find evidence that rising living standards appear to have a time-lagged bidirectional relationship with increasing innovation.

The wealth -> life history -> innovation account of the industrial revolution is largely inconsistent with empirical time series data.

2018 ◽  
Author(s):  
Michael E. W. Varnum ◽  
Igor Grossmann
Keyword(s):  
Time Series ◽  
Life History ◽  
Industrial Revolution ◽  
Time Series Data ◽  
Living Standards ◽  
Life History Strategies ◽  
Series Data ◽  
Bidirectional Relationship ◽  
Limited Support ◽  
Dramatic Rise

Baumard proposes a model to explain the dramatic rise in innovation that occurred during the Industrial Revolution, whereby rising living standards led to slower life history strategies, which in turn fostered innovation. We test his model explicitly using time series data, finding limited support for these proposed linkages. Instead, we find evidence that rising living standards appear to have a bidirectional relationship with increasing innovation.

Life history theory predicts long-term fish assemblage response to stream impoundment

2017 ◽  
Vol 74 (2) ◽  
pp. 228-239 ◽  
Author(s):  
Joshuah S. Perkin ◽  
Natalie E. Knorp ◽  
Thomas C. Boersig ◽  
Amy E. Gebhard ◽  
Lucas A. Hix ◽  
...  
Keyword(s):  
Life History ◽  
Fish Assemblage ◽  
Life History Theory ◽  
Fish Assemblages ◽  
Time Series Data ◽  
Life History Strategies ◽  
Series Data ◽  
Flow Variability ◽  
Downstream Flow ◽  
Stream Impoundment

Life history theory predictions for hydrologic filtering of fish assemblages are rarely tested with historical time series data. We retrospectively analyzed flow regime and fish assemblage data from the Sabine River, USA, to test relationships between life history strategies and hydrologic variability altered by impoundment construction. Downstream flow variability, but not magnitude, was altered by completion of Toledo Bend Reservoir (TBR) in 1966. Consistent with life history theory, occurrence of opportunistic strategists declined while equilibrium strategists increased as the fish assemblage was transformed between periods immediately after (1967–1973) and approximately one decade after (1979–1982) completion of TBR. Assemblage transformation was related to decline of opportunistic strategists throughout 250 km of river downstream of TBR. Temporal trajectories for opportunistic and intermediate strategist richness modelled as a function of flow variability converged 12 years postimpoundment. The spatiotemporal scaling of our study is novel among tests of life history theory, and results suggest impoundment-induced alteration to natural hydrologic filtering of fish assemblages can operate on the scale of hundreds of stream kilometres and manifest within approximately one decade.

scholarly journals Generating Accurate Personalized Predictions of Future Behavior: A Smoking Exemplar

2019 ◽  
Author(s):  
Aaron Jason Fisher ◽  
Peter D. Soyster
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Time Series Data ◽  
Area Under The Curve ◽  
Time Of Day ◽  
Series Data ◽  
Future Behavior ◽  
Out Of Sample ◽  
Time Stamps ◽  
Time Lagged

The present study sought to apply statistical classification methods to idiographic time series data in order to make accurate future predictions of behavior. We recruited 70 individuals who presented as regular smokers; 52 completed experience sampling method (ESM) data collection and provided sufficient time series data. Time stamps from ESM surveys were used to calculate the time of day, day of the week, and continuous time—where the last datum was, in turn, used to calculate 12-hr and 24-hr cycles. Each individual’s time series was split into sequential training and testing sections, so that trained models could be tested on future observations. Prediction models were trained on the first 75% of the individual’s data and tested on the last 25%. Predictions of future behavior were made on a person by person basis. Two prediction algorithms were employed, elastic net regularization and naïve Bayes classification. Sample-wide area under the curve was nearly 80%, with some models demonstrating perfect prediction accuracies. Sensitivity and specificity were between 0.78 and 0.81 across the two approaches. Importantly, prediction models were based on a lagged data structure. Thus, in addition to supporting the prediction accuracy of our models with out-of-sample tests in time-forward data, the models themselves were time-lagged, such that each prediction was for the subsequent measurement. Such a system could be the basis for mobile, just-in-time interventions for substance use, as models that accurately predict future behavior could ostensibly be used for delivering personalized interventions at empirically-indicated moments of need.

scholarly journals Transfer functions: learning about a lagged exposure-outcome association in time-series data

2021 ◽  
Author(s):  
Hiroshi Mamiya ◽  
Alexandra M. Schmidt ◽  
Erica E. M. Moodie ◽  
David L. Buckeridge
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Transfer Functions ◽  
Environmental Research ◽  
Series Data ◽  
Time Lags ◽  
Food Purchasing ◽  
Lag Model ◽  
Time Lagged ◽  
Sugary Beverages

AbstractMany population exposures in time-series analysis, including food marketing, exhibit a time-lagged association with population health outcomes such as food purchasing. A common approach to measuring patterns of associations over different time lags relies on a finite-lag model, which requires correct specification of the maximum duration over which the lagged association extends. However, the maximum lag is frequently unknown due to the lack of substantive knowledge or the geographic variation of lag length. We describe a time-series analytical approach based on an infinite lag specification under a transfer function model that avoids the specification of an arbitrary maximum lag length. We demonstrate its application to estimate the lagged exposure-outcome association in food environmental research: display promotion of sugary beverages with lagged sales.

scholarly journals A framework for assessing frequency domain causality in physiological time series with instantaneous effects

2013 ◽  
Vol 371 (1997) ◽  
pp. 20110618 ◽  
Author(s):  
Luca Faes ◽  
Silvia Erla ◽  
Alberto Porta ◽  
Giandomenico Nollo
Keyword(s):  
Time Series ◽  
Frequency Domain ◽  
Time Series Data ◽  
Model Identification ◽  
Series Data ◽  
Multiple Time ◽  
Multiple Series ◽  
Lagged Effects ◽  
Time Lagged ◽  
Eeg Recordings

We present an approach for the quantification of directional relations in multiple time series exhibiting significant zero-lag interactions. To overcome the limitations of the traditional multivariate autoregressive (MVAR) modelling of multiple series, we introduce an extended MVAR (eMVAR) framework allowing either exclusive consideration of time-lagged effects according to the classic notion of Granger causality, or consideration of combined instantaneous and lagged effects according to an extended causality definition. The spectral representation of the eMVAR model is exploited to derive novel frequency domain causality measures that generalize to the case of instantaneous effects the known directed coherence (DC) and partial DC measures. The new measures are illustrated in theoretical examples showing that they reduce to the known measures in the absence of instantaneous causality, and describe peculiar aspects of directional interaction among multiple series when instantaneous causality is non-negligible. Then, the issue of estimating eMVAR models from time-series data is faced, proposing two approaches for model identification and discussing problems related to the underlying model assumptions. Finally, applications of the framework on cardiovascular variability series and multichannel EEG recordings are presented, showing how it allows one to highlight patterns of frequency domain causality consistent with well-interpretable physiological interaction mechanisms.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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