An Index-Based Time-Series Subsequence Matching Under Time Warping

2006 ◽  
pp. 1043-1050 ◽  
Author(s):  
Byoungchol Chang ◽  
Jaehyuk Cha ◽  
Sang-Wook Kim ◽  
Miyoung Shin
Keyword(s):  
Time Series ◽  
Time Warping ◽  
Subsequence Matching

scholarly journals On Extending the Prefix-Querying Method for Efficient Time-Series Subsequence Matching Under Time Warping

2006 ◽  
Vol 13D (3) ◽  
pp. 357-368
Author(s):  
Byoung-Chol Chang ◽  
Sang-Wook Kim ◽  
Jae-Hyuk Cha
Keyword(s):  
Time Series ◽  
Time Warping ◽  
Subsequence Matching

scholarly journals Efficient Subsequence Search on Streaming Data Based on Time Warping Distance

1970 ◽  
Vol 5 (1) ◽  
pp. 2-8 ◽  
Author(s):  
Sura Rodpongpun ◽  
Vit Niennattrakul ◽  
Chotirat Ann Ratanamahatana
Keyword(s):  
Time Series ◽  
Similarity Search ◽  
Time Series Data ◽  
Distance Measure ◽  
Computational Cost ◽  
Streaming Data ◽  
Series Data ◽  
Search Problem ◽  
Time Warping ◽  
Subsequence Matching

Many algorithms have been proposed to deal with subsequence similarity search problem in time series data stream. Dynamic Time Warping (DTW), which has been accepted as the best distance measure in time series similarity search, has been used in many research works. SPRING and its variance were proposed to solve such problem by mitigating the complexity of DTW. Unfortunately, these algorithms produce meaningless result since no normalization is taken into account before the distance calculation. Recently, GPUs and FPGAs were used in similarity search supporting subsequence normalization to reduce the computation complexity, but it is still far from practical use. In this work, we propose a novel Meaningful Subsequence Matching (MSM) algorithm which produces meaningful result in subsequence matching by considering global constraint, uniform scaling, and normalization. Our method significantly outperforms the existing algorithms in terms of both computational cost and accuracy.

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.

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