scholarly journals Direct At-Sea Observations of Elephant Seals (Mirounga spp.) to Help Interpret Digital Bio-logging Data

2020 ◽  
Vol 8 (1) ◽  
pp. 1-5
Author(s):  
John van den Hoff ◽  
Sam Thalmann
Keyword(s):  
Marine Mammal ◽  
Time Series Data ◽  
Elephant Seal ◽  
Series Data ◽  
Large Prey ◽  
Mammal Species ◽  
Elephant Seals ◽  
Stomach Temperature ◽  
Direct Observations ◽  
Digital Time

Background: A key short-fall with animal-borne bio-logging instruments, which collect digital time-series data regarding the foraging behaviours of cryptic marine mammal species, is validating those data against in situ behaviours. Objective: To collate direct observations of elephant seal feeding behaviour to help interpret foraging behaviours inferred from Time-Depth Recorder (TDR) data. Methods: Direct observations of elephant seal foraging behaviour were collated from the published literature using a search of the world-wide-web. Those observations were supplemented with an unpublished record. Results: Two deep-sea video recordings and six surface sightings of elephant seals ingesting prey were collated. Each observation either supported or suggested an alternative to behaviours derived from digital time-depth profiles. The tendency for elephant seals to surface following the capture of large prey suggests precipitous drops in stomach temperature at the sea-surface, which have been recorded and interpreted as drinking events, more likely represent the ingestion of large prey items. Conclusion: Direct observations of marine mammal foraging behaviours are rare, yet they provide a means to continuously evaluate and interpret outcomes of bio-logging instruments.

Isolation and cross-species amplification of novel microsatellite loci in a charismatic marine mammal species, the northern elephant seal (Mirounga angustirostris)

2012 ◽  
Vol 5 (1) ◽  
pp. 93-96 ◽  
Author(s):  
Simona Sanvito ◽  
Alejandro Dueñes Meza ◽  
Yolanda Schramm ◽  
Pedro Cruz Hernández ◽  
Yareli Esquer Garrigos ◽  
...  
Keyword(s):  
Marine Mammal ◽  
Microsatellite Loci ◽  
Elephant Seal ◽  
Mirounga Angustirostris ◽  
Mammal Species ◽  
Northern Elephant Seal

THE INTERPOLATION ERROR OPERATOR: TIME SERIES ERROR DETECTION AND CORRECTION

Geophysics ◽  
1977 ◽  
Vol 42 (4) ◽  
pp. 773-777 ◽  
Author(s):  
Douglas Nyman
Keyword(s):  
Time Series ◽  
Error Detection ◽  
Time Series Data ◽  
Interpolation Error ◽  
Series Data ◽  
Interpolation Operator ◽  
Data Errors ◽  
Error Detection And Correction ◽  
Digital Time ◽  
Erroneous Data

The interpolation error operator is an effective tool for the detection of digital time series data errors characterized by discontinuities in the function itself or its low order derivatives. The generalized interpolation operator of multiplicity n simultaneously interpolates n consecutive terms. This operator is effective in correcting short lengths of erroneous data.

scholarly journals Bayesian inference of natural selection from allele frequency time series

2016 ◽  
Author(s):  
Joshua Schraiber ◽  
Steven N. Evans ◽  
Montgomery Slatkin
Keyword(s):  
Time Series ◽  
Natural Selection ◽  
Allele Frequency ◽  
Software Package ◽  
Bayesian Method ◽  
Time Series Data ◽  
Coat Color ◽  
Demographic History ◽  
Series Data ◽  
Direct Observations

The advent of accessible ancient DNA technology now allows the direct ascertainment of allele frequencies in ancestral populations, thereby enabling the use of allele frequency time series to detect and estimate natural selection. Such direct observations of allele frequency dynamics are expected to be more powerful than inferences made using patterns of linked neutral variation obtained from modern individuals. We developed a Bayesian method to make use of allele frequency time series data and infer the parameters of general diploid selection, along with allele age, in non-equilibrium populations. We introduce a novel path augmentation approach, in which we use Markov chain Monte Carlo to integrate over the space of allele frequency trajectories consistent with the observed data. Using simulations, we show that this approach has good power to estimate selection coefficients and allele age. Moreover, when applying our approach to data on horse coat color, we find that ignoring a relevant demographic history can significantly bias the results of inference. Our approach is made available in a C++ software package.

Watermarking Audio Signals for Copyright Protection Using ICA

2010 ◽  
pp. 144-157 ◽  
Author(s):  
B. R. Matam ◽  
David Lowe
Keyword(s):  
Time Series ◽  
Copyright Protection ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Audio Signals ◽  
Copyright Information ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Digital Time

The protection or tracking of content transmitted via digital time series data is an important and under-researched area of watermarking. In this chapter a discussion of information hiding in the context of copyright protection of audio signals, an example of time series data is presented. Independent component analysis (ICA) based watermarking methods are used to embed copyright information. The integrity of a hidden message when the cover text in which it is hidden, is attacked by applying signal processing techniques such as filtering and addition of noise to the signal will be investigated. The results of the application of the ICA based method are compared with the results of the application of the discrete wavelet transform (DWT) based approach. This chapter reveals the advantages of using a data dependent transform (for example ICA) based watermarking method for copyright applications when compared with static transform domain (having fixed coefficients, for example DWT) based methods.

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

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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.

Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽  
2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Budiandru Budiandru ◽  
Sari Yuniarti
Keyword(s):  
Interest Rates ◽  
Stock Price ◽  
Time Series Data ◽  
Series Data ◽  
Islamic Banking ◽  
Vector Error Correction ◽  
Investment Financing ◽  
Vector Error ◽  
Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

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