Adaptive cost dynamic time warping distance in time series analysis for classification

The time-series analysis of multi-temporal satellite data is widely used for vegetation regrowth after a wildfire event. Comparisons between pre- and post-fire conditions are the main method used to monitor ecosystem recovery. In the present study, we estimated wildfire disturbance by comparing actual post-fire time series of Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and simulated MODIS EVI based on an artificial neural network assuming no wildfire occurrence. Then, we calculated the similarity of these responses for all sampling sites by applying a dynamic time warping technique. Finally, we applied multidimensional scaling to the warping distances and an optimal fuzzy clustering to identify unique patterns in vegetation recovery. According to the results, artificial neural networks performed adequately, while dynamic time warping and the proposed multidimensional scaling along with the optimal fuzzy clustering provided consistent results regarding vegetation response. For the first two years after the wildfire, medium-high- to high-severity burnt sites were dominated by oaks at elevations greater than 200 m, and presented a clustered (predominant) response of revegetation compared to other sites.

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dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R

Journal of Statistical Software ◽

10.18637/jss.v088.i05 ◽

2019 ◽

Vol 88 (5) ◽

Cited By ~ 9

Author(s):

Victor Maus ◽

Gilberto Câmara ◽

Marius Appel ◽

Edzer Pebesma

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Dynamic Time Warping ◽

Satellite Image ◽

Time Warping ◽

Series Analysis ◽

Dynamic Time

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A Quantum-inspired Entropic Kernel for Multiple Financial Time Series Analysis

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/614 ◽

2020 ◽

Author(s):

Lu Bai ◽

Lixin Cui ◽

Yue Wang ◽

Yuhang Jiao ◽

Edwin R. Hancock

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Similarity Measure ◽

Dynamic Time Warping ◽

Financial Time Series ◽

Time Varying ◽

Time Warping ◽

Classical Dynamic ◽

Financial Time ◽

Dynamic Time

Network representations are powerful tools for the analysis of time-varying financial complex systems consisting of multiple co-evolving financial time series, e.g., stock prices, etc. In this work, we develop a new kernel-based similarity measure between dynamic time-varying financial networks. Our ideas is to transform each original financial network into quantum-based entropy time series and compute the similarity measure based on the classical dynamic time warping framework associated with the entropy time series. The proposed method bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks abstracted from financial time series of New York Stock Exchange (NYSE) database demonstrate that our approach can effectively discriminate the abrupt structural changes in terms of the extreme financial events.

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

Computational Economics ◽

10.1007/s10614-021-10103-y ◽

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.

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

Energies ◽

10.3390/en14134024 ◽

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.

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