scholarly journals Opportunity activity sequence investigations in B2B CRM systems

2020 ◽  
Vol 12 (1) ◽  
pp. 70-83
Author(s):  
Doru Rotovei
Keyword(s):  
Time Series ◽  
Business Environment ◽  
Customer Relationship ◽  
Time Warping ◽  
Time Data ◽  
Data Set ◽  
Phone Calls ◽  
Prospective Buyer ◽  
System Data ◽  
Dynamic Time

AbstractClosing a deal in a business to business environment implies a series of orchestrated actions that the sales representatives are taking to take a prospective buyer from first contact to a closed sale. The actions, such as meetings, emails, phone calls happen in succession and in different points in time relative to the first interaction.Time-series are ordered sequences of discrete-time data. In this work, we are examining the relationship between the actions as time series and the final win outcome for each deal. To assess whether the behavior of the salespeople have a direct influence on the final outcome of the current deal, we used histogram analysis, dynamic time warping and string edit distance on a real-world Customer Relationship Management System data set. The results are discussed and included in this paper.

scholarly journals Consensus Patterns of a Set of Time Series via a Wavelet-Based Temporal Localization: Emphasizing the Utility over Point-Wise Averaging and Averaging under Dynamic Time Warping

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Chekhaprabha Priyadarshanee Waduge ◽  
Naleen Chaminda Ganegoda ◽  
Darshana Chitraka Wickramarachchi ◽  
Ravindra Shanthakumar Lokupitiya
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Haar Wavelet ◽  
Temporal Variations ◽  
Sequential Data ◽  
Time Warping ◽  
Data Set ◽  
The Mean ◽  
Dynamic Time ◽  
Temporal Localization

Summarizing or averaging a sequential data set (i.e., a set of time series) can be comprehensively approached as a result of sophisticated computational tools. Averaging under Dynamic Time Warping (DTW) is one such tool that captures consensus patterns. DTW acts as a similarity measure between time series, and subsequently, an averaging method must be executed upon the behaviour of DTW. However, averaging under DTW somewhat neglects temporal aspect since it is on the search of similar appearances rather than stagnating on corresponding time-points. On the contrary, the mean series carrying point-wise averages provides only a weak consensus pattern as it may over-smooth important temporal variations. As a compromise, a pool of consensus series termed Ultimate Tamed Series (UTS) is studied here that adheres to temporal decomposition supported by the discrete Haar wavelet. We claim that UTS summarizes localized patterns, which would not be reachable via the series under DTW or the mean series. Neighbourhood of localization can be altered as a user can customize different levels of decomposition. In validation, comparisons are carried out with the series under DTW and the mean series via Euclidean distance and the distance resulted by DTW itself. Two sequential data sets are selected for this purpose from a standard repository.

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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