scholarly journals Estimation of COVID-19 dynamics in the different states of the United States using Time-Series Clustering

2020 ◽  
Author(s):  
Fernando Rojas ◽  
Olga Valenzuela ◽  
Ignacio Rojas
Keyword(s):  
United States ◽  
Time Series ◽  
Dynamic Time Warping ◽  
Hierarchical Cluster ◽  
The United States ◽  
Time Warping ◽  
Precise Knowledge ◽  
Eastern Zone ◽  
Western Zone ◽  
Dynamic Time

AbstractEstimation of COVID-19 dynamics and its evolution is a multidisciplinary effort, which requires the unification of heterogeneous disciplines (scientific, mathematics, epidemiological, biological/bio-chemical, virologists and health disciplines to mention the most relevant) to work together in a better understanding of this pandemic. Time series analysis is of great importance to determine both the similarity in the behavior of COVID-19 in certain countries/states and the establishment of models that can analyze and predict the transmission process of this infectious disease. In this contribution, an analysis of the different states of the United States will be carried out to measure the similarity of COVID-19 time series, using dynamic time warping distance (DTW) as a distance metric. A parametric methodology is proposed to jointly analyze infected and deceased persons. This metric allows to compare time series that have a different time length, making it very appropriate for studying the United States, since the virus did not spread simultaneously in all the states/provinces. After a measure of the similarity between the time series of the states of United States was determined, a hierarchical cluster was created, which makes it possible to analyze the behavioral relationships of the pandemic between different states and to discover interesting patterns and correlations in the underlying data of COVID-19 in the United States. With the proposed methodology, nine different clusters were obtained, showing a different behavior in the eastern zone and western zone of the United States. Finally, to make a prediction of the evolution of COVID-19 in the states, Logistic, Gompertz and SIR model was computed. With these mathematical model it is possible to have a more precise knowledge of the evolution and forecast of the pandemic.

scholarly journals Estimation of COVID-19 Dynamics in the Different States of the United States during the First Months of the Pandemic

2021 ◽  
Vol 5 (1) ◽  
pp. 53
Author(s):  
Ignacio Rojas-Valenzuela ◽  
Olga Valenzuela ◽  
Elvira Delgado-Marquez ◽  
Fernando Rojas
Keyword(s):  
United States ◽  
Time Series ◽  
Dynamic Time Warping ◽  
Hierarchical Cluster ◽  
The United States ◽  
Time Warping ◽  
Precise Knowledge ◽  
Eastern Zone ◽  
Western Zone ◽  
Dynamic Time

Estimation of COVID-19 dynamics and its evolution is a multidisciplinary effort, which requires the unification of heterogeneous disciplines (scientific, mathematics, epidemiological, biological/bio-chemical, virologists and health disciplines to mention the most relevant) to work together towards a better understanding of this pandemic. Time series analysis is of great importance to determine both the similarity in the behavior of COVID-19 in certain countries/states and the establishment of models that can analyze and predict the transmission process of this infectious disease. In this contribution, an analysis of the different states of the United States will be carried out to measure the similarity of COVID-19 time series, using dynamic time warping distance (DTW) as a distance metric. A parametric methodology is proposed to jointly analyze infected and deceased persons. This metric allows comparison of time series that have a different time length, making it very appropriate for studying the United States, since the virus did not spread simultaneously in all the states/provinces. After a measure of the similarity between the time series of the states of United States was determined, a hierarchical cluster was created, which makes it possible to analyze the behavioral relationships of the pandemic between different states and to discover interesting patterns and correlations in the underlying data of COVID-19 in the United States. With the proposed methodology, nine different clusters were obtained, showing a different behavior in the eastern zone and western zone of the United States. Finally, to make a prediction of the evolution of COVID-19 in the states, Logistic, Gompertz and SIR models were computed. With these mathematical models, it is possible to have a more precise knowledge of the evolution and forecast of the pandemic.

scholarly journals New DTW Windows Type for Forward- and Backward-Lookingness Examination. Application for Inflation Expectation

2021 ◽  
Author(s):  
Aleksandra Rutkowska ◽  
Magdalena Szyszko
Keyword(s):  
Time Series ◽  
Information Content ◽  
Dynamic Time Warping ◽  
The Novel ◽  
Time Warping ◽  
Specific Question ◽  
Inflation Expectation ◽  
Standard Tool ◽  
Dynamic Time ◽  
Research Period

AbstractThis study provides an application of dynamic time warping algorithm with a new window constraint to assess consumer expectations’ information content regarding current and future inflation. Our study’s contribution is the novel application of DTW for testing expectations’ forward-lookingness. Additionally, we modify the algorithm to adjust it for a specific question on the information content of our data. The DTW overcomes constraints of the standard tool that examines forward-lookingness: DTW does not impose assumptions on time series properties. In empirical study we cover seven European counties and compare the DTW outcomes with the results of previous studies in these economies using a standard methodology. The research period covers 2001 to mid-2018. Application of DTW provides information on the degree of expectations’ forward-lookingness. The result, after standardization, are similar to the standard parameters of hybrid specification of expectations. Moreover, the rankings of most forward-looking consumers are replicated. Our results confirm the economic intuition, and they do not contradict previous studies.

scholarly journals The Connections between COVID-19 and the Energy Commodities Prices: Evidence through the Dynamic Time Warping Method

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4024
Author(s):  
Krzysztof Dmytrów ◽  
Joanna Landmesser ◽  
Beata Bieszk-Stolorz
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Dynamic Time Warping ◽  
Global Economy ◽  
Price Change ◽  
Commodity Prices ◽  
The Other ◽  
Time Warping ◽  
Heating Oil ◽  
Dynamic Time

The main objective of the study is to assess the similarity between the time series of energy commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic affects all aspects of the global economy. Although this impact is multifaceted, we assess the connections between the number of COVID-19 cases and the energy commodities sector. We analyse these connections by using the Dynamic Time Warping (DTW) method. On this basis, we calculate the similarity measure—the DTW distance between the time series—and use it to group the energy commodities according to their price change. Our analysis also includes finding the time shifts between daily COVID-19 cases and commodity prices in subperiods according to the chronology of the COVID-19 pandemic. Our findings are that commodities such as ULSD, heating oil, crude oil, and gasoline are weakly associated with COVID-19. On the other hand, natural gas, palm oil, CO2 allowances, and ethanol are strongly associated with the development of the pandemic.

scholarly journals Satellite Image Time Series Clustering via Time Adaptive Optimal Transport

2021 ◽  
Vol 13 (19) ◽  
pp. 3993
Author(s):  
Zheng Zhang ◽  
Ping Tang ◽  
Weixiong Zhang ◽  
Liang Tang
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Dynamic Time Warping ◽  
Optimal Transport ◽  
Satellite Image ◽  
Time Warping ◽  
Unsupervised Analysis ◽  
Training Samples ◽  
Dynamic Time ◽  
Visual Results

Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised analysis methods need to be developed. Similarity measure is critical for clustering, however, currently established methods represented by Dynamic Time Warping (DTW) still exhibit several issues when coping with SITS, such as pathological alignment, sensitivity to spike noise, and limitation on capacity. In this paper, we introduce a new time series similarity measure method named time adaptive optimal transport (TAOT) to the application of SITS clustering. TAOT inherits several promising properties of optimal transport for the comparing of time series. Statistical and visual results on two real SITS datasets with two different settings demonstrate that TAOT can effectively alleviate the issues of DTW and further improve the clustering accuracy. Thus, TAOT can serve as a usable tool to explore the potential of precious SITS data.

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