scholarly journals Mutual relationships between the unemployment rate and the unemployment duration in the Visegrad Group countries in years 2001–2017

Equilibrium ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 129-148 ◽  
Author(s):  
Krzysztof Dmytrów ◽  
Beata Bieszk-Stolorz
Keyword(s):  
Unemployment Rate ◽  
Dynamic Time Warping ◽  
Clock Cycle ◽  
Value Added ◽  
Unemployment Duration ◽  
Time Warping ◽  
The Business Cycle ◽  
Dynamic Time ◽  
The Relationship ◽  
Visegrad Group

Research background: The most important indicators that describe the situation on the labour market are the unemployment rate and the unemployment duration. If both these indicators are high, then the human capital deteriorates. Therefore, it seems justified to analyse the mutual relationships between them. Purpose of the article: The article aims at finding the relationships between the unemployment rate and the unemployment duration, and checking if the mutual courses of these two indicators in the Visegrad Group countries are connected with each other. Methods: The business cycle clock methodology will be used to analyse the relationship between the unemployment rate and the median unemployment duration. Next, the similarity of the course of these two indicators will be analysed by means of the Pearson product-moment correlation coefficient and the Dynamic Time Warping (DTW) technique. Findings & Value added: Amongst the analysed countries, Czechia, Poland and Slovakia were, to a certain degree, similar with respect to the mutual course of the unemployment rate and the unemployment duration. Until the peak of the financial crisis in 2009, the unemployment rate and the unemployment duration decreased. During the next years, the unemployment rate was increasing and after 2-3 years it was followed by the increase of the unemployment duration. The situation improved after the year 2013 — both indicators were decreasing. In Hungary, on the contrary, the unemployment rate was increasing or steady until 2012, and during the following years it started to decrease. However, the course of the unemployment duration was completely different than in remaining countries. The value added of the article is application of the business clock cycle and the Dynamic Time Warping technique in finding the relationships and similarity of courses between the unemployment rate and the unemployment duration.

Segmented Dynamic Time Warping

2019 ◽  
pp. 1-19
Author(s):  
Ruizhe Ma ◽  
Azim Ahmadzadeh ◽  
Soukaina Filali Boubrahimi ◽  
Rafal A Angryk
Keyword(s):  
Dynamic Time Warping ◽  
Time Series Data ◽  
Series Data ◽  
Time Warping ◽  
Use Of Time ◽  
Computational Speed ◽  
Selection Parameters ◽  
And Performance ◽  
Dynamic Time ◽  
The Relationship

Initially used in speech recognition, the dynamic time warping algorithm (DTW) has regained popularity with the widespread use of time series data. While demonstrating good performance, this elastic measure has two significant drawbacks: high computational costs and the possibility of pathological warping paths. Due to the balance between performance and the tightness of restrictions, the effects of many improvement techniques are either limited in effect or use accuracy as a trade-off. In this chapter, the authors discuss segmented-DTW (segDTW) and its applications. The intuition behind significant features is first established. Then considering the variability of different datasets, the relationship between specific global feature selection parameters, feature numbers, and performance are demonstrated. Other than the improvement in computational speed and scalability, another advantage of segDTW is that while it can be a stand-alone heuristic, it can also be easily combined with other DTW improvement methods.

scholarly journals Analysis of Shooting Consistency in Archers: A Dynamic Time Warping Algorithm-Based Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-6
Author(s):  
Cheng-Hao Quan ◽  
Zia Mohy-Ud-Din ◽  
Sangmin Lee
Keyword(s):  
Dynamic Time Warping ◽  
Important Determinant ◽  
Small Distance ◽  
Elementary Level ◽  
Time Warping ◽  
Acceleration Data ◽  
Dynamic Time ◽  
High Level ◽  
The Relationship ◽  
Inertia Sensor

The shooting consistency of an archer is commonly perceived to be an important determinant of successful scores. Four (n=4) elementary level archers from a middle school in Korea participated in this study. In order to quantify shooting consistency, movement of the bow forearm was measured with an inertia sensor during archery shooting. The shooting consistency was calculated and defined by the dynamic time warping (DTW) algorithm as the distance between two time sequences of acceleration data. Small distance values indicate that the archer has maintained high-level shooting consistency while archery shooting repetitively. To verify the shooting consistency metric, the relationship between scores and shooting consistency is evaluated. The results show that the higher the scores achieved by the archer, the higher is the level of shooting consistency demonstrated.

Abstract 2850: Morphological Variability: A New Electrocardiographic Technique for Risk Stratification After NSTEACS

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
Zeeshan Syed ◽  
Collin M Stultz ◽  
Benjamin M Scirica ◽  
Christopher P Cannon ◽  
Khaled Attia ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Stratification ◽  
Dynamic Time Warping ◽  
Morphological Variability ◽  
Time Warping ◽  
Complete Evaluation ◽  
Non Invasive ◽  
Dynamic Time ◽  
The Relationship ◽  
Recurrent Ischemia

Background : ECG parameters such as low heart rate variability (HRV) identify patients at high risk post-ACS. We recently developed morphological variability (MV), a novel technique that quantifies differences in the morphology of entire beats using a dynamic time-warping algorithm. MV incorporates strictly more information than HRV, potentially offering a more complete evaluation of the ECG. We assessed the relationship among MV, HRV, and outcomes after NSTEACS. Methods: MV and HRV were calculated in 863 pts from the DISPERSE2 trial using the first 24 hrs of continuous ECG (CECG) after randomization for NSTEACS. Using each measure, pts were split into high and low variability groups (cutpoint for HRV (SDNN)= 75ms and for MV=0.7). Ischemia on CECG was defined as ≥1mm ST dep lasting ≥1min. Results: A total of 144 (16.7%) pts had high MV and 58 (6.7%) had low HRV. Pts with high MV experienced higher rates of death, death/MI/severe recurrent ischemia (SRI), and ischemia detected on CECG compared to low MV. (Table-Figure ) This relationship remained consistent in pts with no ischemia on CECG (hazard ratio for D/MI/SRI =2.5, p=0.016). There was no difference in mortality or ischemia on CECG in pts with low HRV v high HRV, but pts with low HRV did have higher rates of death/MI/SRI. (Table) Conclusions: MV correlates significantly with poor cardiovascular outcomes, including death, after NSTEACS, even after controlling for other high risk features and even among pts without electrocardiographic evidence of ischemia. MV may offer a new non-invasive measure for risk stratification after ACS.

scholarly journals Multivariate Time Series Data Clustering Method Based on Dynamic Time Warping and Affinity Propagation

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaoji Wan ◽  
Hailin Li ◽  
Liping Zhang ◽  
Yenchun Jim Wu
Keyword(s):  
Time Series ◽  
Correlation Matrix ◽  
Dynamic Time Warping ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Affinity Propagation ◽  
Series Data ◽  
Time Warping ◽  
Dynamic Time ◽  
The Relationship

In view of the importance of various components and asynchronous shapes of multivariate time series, a clustering method based on dynamic time warping and affinity propagation is proposed. From the two perspectives of the global and local properties information of multivariate time series, the relationship between the data objects is described. It uses dynamic time warping to measure the similarity between original time series data and obtain the similarity between the corresponding components. Moreover, it also uses the affinity propagation to cluster based on the similarity matrices and, respectively, establishes the correlation matrices for various components and the whole information of multivariate time series. In addition, we further put forward the synthetical correlation matrix to better reflect the relationship between multivariate time series data. Again the affinity propagation algorithm is applied to clustering the synthetical correlation matrix, which realizes the clustering analysis of the original multivariate time series data. Numerical experimental results demonstrate that the efficiency of the proposed method is superior to the traditional ones.

scholarly journals The use of the dynamic time warping (DTW) method to describe the COVID-19 dynamics in Poland

2021 ◽  
Vol 12 (3) ◽  
pp. 539-556
Author(s):  
Joanna Landmesser
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Local Level ◽  
Value Added ◽  
Decision Makers ◽  
Time Warping ◽  
Disease Evolution ◽  
Long Run ◽  
Dynamic Time ◽  
Medical Infrastructure

Research background: In recent times, the whole world has been severely affected by the COVID-19 pandemic. The influence of the epidemic on the society and the economy has caused a great deal of scientific interest. The development of the pandemic in many countries was analyzed using various models. However, the literature on the dissemination of COVID-19 lacks econometric analyzes of the development of this epidemic in Polish voivodeships. Purpose of the article: The aim of the study is to find similarities in time series for infected with and those who died of COVID-19 in Polish voivodeships using the method of dynamic time warping. Methods: The dynamic time warping method allows to calculate the distance between two time series of different lengths. This feature of the method is very important in our analysis because the coronavirus epidemic did not start in all voivodeships at the same time. The dynamic time warping also enables an adjustment of the timeline to find similar, but shifted, phases. Using this method, we jointly analyze the number of infected and deceased people in each province. In the next step, based on the measured similarity of the time series, the voivodeships are grouped hierarchically. Findings & value added: We use the dynamic time warping to identify groups of voivodeships affected by the epidemic to a different extent. The classification performed may be useful as it indicates patterns of the COVID-19 disease evolution in Polish voivodeships. The results obtained at the regional level will allow better prediction of future infections. Decision makers should formulate further recommendations for lockdowns at the local level, and in the long run, adjust the medical infrastructure in the regions accordingly. Policymakers in other countries can benefit from the findings by shaping their own regional policies accordingly.

Implementation of Mel Frequency Cepstral Coefficient and Dynamic Time Warping For Bird Sound Classification

2019 ◽  
Vol 5 ◽  
Author(s):  
Hari Prapcoyo ◽  
Bertha Pratama Adhita Putra ◽  
Rifki Indra Perwira
Keyword(s):  
Dynamic Time Warping ◽  
Time Warping ◽  
Sound Classification ◽  
Dynamic Time ◽  
Mel Frequency Cepstral Coefficient

Segmented Dynamic Time Warping for Spoken Query-by-Example Search

2016 ◽  
Author(s):  
Jorge Proença ◽  
Fernando Perdigão
Keyword(s):  
Dynamic Time Warping ◽  
Time Warping ◽  
Query By Example ◽  
Dynamic Time
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