scholarly journals Coder Reliability and Misclassification in the Human Coding of Party Manifestos

2012 ◽  
Vol 20 (1) ◽  
pp. 78-91 ◽  
Author(s):  
Slava Mikhaylov ◽  
Michael Laver ◽  
Kenneth R. Benoit
Keyword(s):  
Time Series ◽  
Content Analysis ◽  
Gold Standard ◽  
Generation Process ◽  
Text Generation ◽  
Data Set ◽  
Policy Positions ◽  
Empirical Estimates ◽  
Party Manifestos ◽  
Party Policy Positions

The Comparative Manifesto Project (CMP) provides the only time series of estimated party policy positions in political science and has been extensively used in a wide variety of applications. Recent work (e.g., Benoit, Laver, and Mikhaylov 2009; Klingemann et al. 2006) focuses on nonsystematic sources of error in these estimates that arise from the text generation process. Our concern here, by contrast, is with error that arises during the text coding process since nearly all manifestos are coded only once by a single coder. First, we discuss reliability and misclassification in the context of hand-coded content analysis methods. Second, we report results of a coding experiment that used trained human coders to code sample manifestos provided by the CMP, allowing us to estimate the reliability of both coders and coding categories. Third, we compare our test codings to the published CMP “gold standard” codings of the test documents to assess accuracy and produce empirical estimates of a misclassification matrix for each coding category. Finally, we demonstrate the effect of coding misclassification on the CMP's most widely used index, its left-right scale. Our findings indicate that misclassification is a serious and systemic problem with the current CMP data set and coding process, suggesting the CMP scheme should be significantly simplified to address reliability issues.

Private Donations and Policy Ambiguity

2018 ◽  
Vol 52 (5) ◽  
pp. 754-776 ◽  
Author(s):  
Christine Cahill ◽  
Andrey Tomashevskiy
Keyword(s):  
Time Series ◽  
Political Parties ◽  
Oecd Countries ◽  
Campaign Contributions ◽  
Data Set ◽  
Policy Positions ◽  
Party Positions ◽  
Political Finance ◽  
Private Donations ◽  
Cross National

An important dimension of party positioning remains largely unexamined—that is, the clarity with which parties present policies to the electorate. Moreover, the effects of private campaign contributions on party positions are also vastly understudied. We address these gaps using a unique new data set on private contributions to political parties in eight Organisation for Economic Co-operation and Development (OECD) countries from the early 1990s to the present. We argue that parties are incentivized to present increasingly ambiguous, or broad appeal, policy positions as a result of increased private campaign contributions. Broad appeal campaigns allow parties to appease their donors with more extreme policy preferences while maintaining the support of their more moderate base supporters. We find support for this argument and show that increasing donations are associated with increased policy ambiguity. Using new data, this article is the first to examine an important connection between political finance and party positioning on a cross-national and time-series basis.

scholarly journals Economic Growth, Inflation and Monetary Policy in Pakistan: Preliminary Empirical Estimates

2021 ◽  
Vol 4 (2) ◽  
pp. 321-333
Author(s):  
Hina Ali ◽  
Malka Liaquat ◽  
Noreen Safdar ◽  
Saeed ur Rahman
Keyword(s):  
Time Series ◽  
Monetary Policy ◽  
Unit Root ◽  
Time Series Data ◽  
Unit Root Test ◽  
Series Data ◽  
Economic Activities ◽  
Data Set ◽  
Explanatory Variables ◽  
Empirical Estimates

In economic policy, construction Inflation is a core variable to be considered that determines the economic activity. To make a suitable monetary policy, it is very essential to check the price level and later on, many other variables are considered to achieve the goal. This study aims to reveal the affiliation of inflation on the growth of economic activities in Pakistan. Time series data set for the period 1989-2020 was used to have the empirical estimates.  Augmented Dickey Fuller Unit Root Test is employed to check the unit root of the time series and Auto Regressive Distributive Lag techniques are used for empirical estimates. The present research uses Inflation as a dependent variable and Gross Domestic Product, Interest Rate, Money Supply, and Exchange Rate as the explanatory variables of the study. The findings of this analysis reveal that there's an antagonistic relation between Inflation and GDP.

EXTREME VALUES OF SEA SURFACE TEMPERATURE ASSOCIATED WITH LONG-PERIOD PHENOMENA OCCURRED DURING 1960-2015 IN THE COLOMBIAN PACIFIC OCEAN

2017 ◽  
Author(s):  
Diaz Juan Navia ◽  
Diaz Juan Navia ◽  
Bolaños Nancy Villegas ◽  
Bolaños Nancy Villegas ◽  
Igor Malikov ◽  
...  
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Extreme Values ◽  
Sea Surface ◽  
Absolute Maximum ◽  
Data Set ◽  
Arctic Oscillation Index ◽  
Colombian Pacific

Sea Surface Temperature Anomalies (SSTA), in four coastal hydrographic stations of Colombian Pacific Ocean, were analyzed. The selected hydrographic stations were: Tumaco (1°48'N-78°45'W), Gorgona island (2°58'N-78°11'W), Solano Bay (6°13'N-77°24'W) and Malpelo island (4°0'N-81°36'W). SSTA time series for 1960-2015 were calculated from monthly Sea Surface Temperature obtained from International Comprehensive Ocean Atmosphere Data Set (ICOADS). SSTA time series, Oceanic Nino Index (ONI), Pacific Decadal Oscillation index (PDO), Arctic Oscillation index (AO) and sunspots number (associated to solar activity), were compared. It was found that the SSTA absolute minimum has occurred in Tumaco (-3.93°C) in March 2009, in Gorgona (-3.71°C) in October 2007, in Solano Bay (-4.23°C) in April 2014 and Malpelo (-4.21°C) in December 2005. The SSTA absolute maximum was observed in Tumaco (3.45°C) in January 2002, in Gorgona (5.01°C) in July 1978, in Solano Bay (5.27°C) in March 1998 and Malpelo (3.64°C) in July 2015. A high correlation between SST and ONI in large part of study period, followed by a good correlation with PDO, was identified. The AO and SSTA have showed an inverse relationship in some periods. Solar Cycle has showed to be a modulator of behavior of SSTA in the selected stations. It was determined that extreme values of SST are related to the analyzed large scale oscillations.

The Lyapunov Exponent Variance of an Electronic Manufacturing Enterprise’s Daily Qualified Rate Time Series by Improved Small Data Sets Approach

2012 ◽  
Vol 197 ◽  
pp. 271-277
Author(s):  
Zhu Ping Gong
Keyword(s):  
Time Series ◽  
Lyapunov Exponent ◽  
Chaotic Time Series ◽  
Quality System ◽  
Quality Level ◽  
Largest Lyapunov Exponent ◽  
Small Data ◽  
Data Set ◽  
Electronic Manufacturing ◽  
Small Data Set

Small data set approach is used for the estimation of Largest Lyapunov Exponent (LLE). Primarily, the mean period drawback of Small data set was corrected. On this base, the LLEs of daily qualified rate time series of HZ, an electronic manufacturing enterprise, were estimated and all positive LLEs were taken which indicate that this time series is a chaotic time series and the corresponding produce process is a chaotic process. The variance of the LLEs revealed the struggle between the divergence nature of quality system and quality control effort. LLEs showed sharp increase in getting worse quality level coincide with the company shutdown. HZ’s daily qualified rate, a chaotic time series, shows us the predictable nature of quality system in a short-run.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

scholarly journals AB1138 ASSESSMENT OF FLUORESCENCE-OPTICAL IMAGING TECHNIQUE OF THE HANDS IN PSORIASIS AND PSORIATIC PATIENTS USING AN INNOVATIVE OBJECTIVE METHOD

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1859.2-1859
Author(s):  
L. Zerweck ◽  
U. Henkemeier ◽  
P. H. Nguyen ◽  
T. Rossmanith ◽  
A. Pippow ◽  
...  
Keyword(s):  
Time Series ◽  
Early Detection ◽  
Optical Imaging ◽  
Skin Diseases ◽  
Imaging Biomarkers ◽  
Research Support ◽  
Scoring Method ◽  
Inflammatory Skin Diseases ◽  
Data Set ◽  
Fluorescence Optical Imaging

Background:Psoriasis (Pso) is one of the most common chronic inflammatory skin diseases in Europe. Psoriatic arthritis (PsA) is closely associated to Pso whereas the skin manifestation appears usually years before PsA-related symptoms emerge. Up to 30% of Pso patients develop PsA, biomarkers for its early detection are of major importance. In early PsA, changes in synovial vascularisation appear first. Imaging biomarkers for detection of changes in vascularisation might be useful for early detection of musculoskeletal disease. Fluorescence-optical imaging (FOI) is a new method to detect changes in microvascularisation of the hands. Each collected data set of the FOI system contains 360 images representing a time progression of the indocyanine green (ICG) distribution.Objectives:To evaluate a reader-independent assessment method for evaluation of FOI in patients with PsO and PsA.Methods:A prospective study including patients with dermatological confirmed skin PsO was performed. 411 patients were included from German dermatology units without PsA diagnosis but potential risk for its development. Clinical examination (CE) was performed by a qualified rheumatologist. For a reader independent evaluation of the FOI images an objective joint-based scoring method was developed. For this method, the joint areas are defined by image segmentation and scored based on generated heatmaps. To calculate a heatmap indicating conspicuous joints from a data set containing 360 images, each pixel is converted to a time series containing 360 values. From this time series, three independent values (features) are extracted: amplitude, average value and maximal slope. Thus, each pixel is reduced to three different feature values. After the three features are determined for each pixel, k-means clustering is performed on each feature. The numbers of centroids (k) are set to 3, 5, 7 and 9. 12 heatmaps (3 features à 4 ks) are calculated, which results in 12 scores for each joint as well. The clusters are then sorted dependent on their centroid value and coloured accordingly to a predefined heatmap colour palette. To finally score each joint, the pixels in the segmented joint area and their assigned cluster are summed and normalized by the area’s amount of pixels and k.Results:271 of the patients were investigated by the newly developed method and compared with the CE scoring. 6426 joints were labeled as healthy whereas 1162 joints were either labeled as swollen, tender or both. The result over all investigated patients for k = 9 is summed in table 1. It is observable that every average and median healthy value is lower than the corresponding affected value.Table 1.Resulting scores for k = 9 for all 271 patients.Feature Statistic valueAmplitudeMeanSlopeHealthyAffectedHealthyAffectedHealthyAffectedAverage0.5030.5280.4860.5090.3950.414Median0.4960.5320.4820.5050.3890.415Conclusion:FOI is an innovative method that detects early changes in vascularization of the hands. So, this method can be useful in early detection of arthritis especially in risk populations such as PsO patients. The results of the objective scoring method show that a clear distinction between healthy and affected joints is possible with the average scores as well as the median values. However, if the range of the scores is considered, the overlap between healthy and affected is not neglectable. Thus, the current scoring system can be used as an indicator but not as a single classification marker. Nevertheless, the research at hand has shown the expected outcome and motivates further development on the heatmap approach.Disclosure of Interests:Lukas Zerweck: None declared, Ulf Henkemeier: None declared, Phuong-Ha Nguyen: None declared, Tanja Rossmanith Grant/research support from: Janssen, BMS, LEO, Pfizer, Andreas Pippow: None declared, Harald Burkhardt Grant/research support from: Pfizer, Roche, Abbvie, Consultant of: Sanofi, Pfizer, Roche, Abbvie, Boehringer Ingelheim, UCB, Eli Lilly, Chugai, Bristol Myer Scripps, Janssen, and Novartis, Speakers bureau: Sanofi, Pfizer, Roche, Abbvie, Boehringer Ingelheim, UCB, Eli Lilly, Chugai, Bristol Myer Scripps, Janssen, and Novartis, Frank Behrens Grant/research support from: Pfizer, Janssen, Chugai, Celgene, Lilly and Roche, Consultant of: Pfizer, AbbVie, Sanofi, Lilly, Novartis, Genzyme, Boehringer, Janssen, MSD, Celgene, Roche and Chugai, Michaela Köhm Grant/research support from: Pfizer, Janssen, BMS, LEO, Consultant of: BMS, Pfizer, Speakers bureau: Pfizer, BMS, Janssen, Novartis

Deep Learning Based Classification of Time Series of Chaotic Systems over Graphic Images

2021 ◽  
Author(s):  
Süleyman UZUN ◽  
Sezgin KAÇAR ◽  
Burak ARICIOĞLU
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Transfer Learning ◽  
Chaotic Systems ◽  
Initial Conditions ◽  
Step Size ◽  
Learning Methods ◽  
Data Set ◽  
Graphic Images ◽  
Different Chaotic Systems

Abstract In this study, for the first time in the literature, identification of different chaotic systems by classifying graphic images of their time series with deep learning methods is aimed. For this purpose, a data set is generated that consists of the graphic images of time series of the most known three chaotic systems: Lorenz, Chen, and Rossler systems. The time series are obtained for different parameter values, initial conditions, step size and time lengths. After generating the data set, a high-accuracy classification is performed by using transfer learning method. In the study, the most accepted deep learning models of the transfer learning methods are employed. These models are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet and GoogLeNet. As a result of the study, classification accuracy is found between 96% and 97% depending on the problem. Thus, this study makes association of real time random signals with a mathematical system possible.

Long term variations of the hydrochemical composition of deep thermal ground water in the Lower Bavarian Molasse Basin – Causes and Perspectives

2021 ◽  
Author(s):  
Annette Dietmaier ◽  
Thomas Baumann
Keyword(s):  
Time Series ◽  
Real Life ◽  
Data Series ◽  
Fluid Composition ◽  
Molasse Basin ◽  
Deep Groundwater ◽  
Data Set ◽  
Groundwater Exploitation ◽  
Groundwater Aquifers

<p>The European Water Framework Directive (WFD) commits EU member states to achieve a good qualitative and quantitative status of all their water bodies.  WFD provides a list of actions to be taken to achieve the goal of good status.  However, this list disregards the specific conditions under which deep (> 400 m b.g.l.) groundwater aquifers form and exist.  In particular, deep groundwater fluid composition is influenced by interaction with the rock matrix and other geofluids, and may assume a bad status without anthropogenic influences. Thus, a new concept with directions of monitoring and modelling this specific kind of aquifers is needed. Their status evaluation must be based on the effects induced by their exploitation. Here, we analyze long-term real-life production data series to detect changes in the hydrochemical deep groundwater characteristics which might be triggered by balneological and geothermal exploitation. We aim to use these insights to design a set of criteria with which the status of deep groundwater aquifers can be quantitatively and qualitatively determined. Our analysis is based on a unique long-term hydrochemical data set, taken from 8 balneological and geothermal sites in the molasse basin of Lower Bavaria, Germany, and Upper Austria. It is focused on a predefined set of annual hydrochemical concentration values. The data range dates back to 1937. Our methods include developing threshold corridors, within which a good status can be assumed, and developing cluster analyses, correlation, and piper diagram analyses. We observed strong fluctuations in the hydrochemical characteristics of the molasse basin deep groundwater during the last decades. Special interest is put on fluctuations that seem to have a clear start and end date, and to be correlated with other exploitation activities in the region. For example, during the period between 1990 and 2020, bicarbonate and sodium values displayed a clear increase, followed by a distinct dip to below-average values and a subsequent return to average values at site F. During the same time, these values showed striking irregularities at site B. Furthermore, we observed fluctuations in several locations, which come close to disqualifying quality thresholds, commonly used in German balneology. Our preliminary results prove the importance of using long-term (multiple decades) time series analysis to better inform quality and quantity assessments for deep groundwater bodies: most fluctuations would stay undetected within a < 5 year time series window, but become a distinct irregularity when viewed in the context of multiple decades. In the next steps, a quality assessment matrix and threshold corridors will be developed, which take into account methods to identify these fluctuations. This will ultimately aid in assessing the sustainability of deep groundwater exploitation and reservoir management for balneological and geothermal uses.</p>

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