Harsha Gwalani
ABSTRACT
Dynamic Time Warping (DTW) is an asynchronous alignment algorithm used to measure similarities between temporal sequences. DTW is advantageous for comparing sequences when the shape of the pattern is more important than the speed of events. It has been widely used for automated speech recognition, pattern detection in stock pricing data, studying energy consumption patterns for appliances, etc. In this presentation, we discuss the use of dynamic time warping for understanding long-term usage patterns on Twitter. Time series data are more useful to understand repeat user behavior and build user narratives than aggregated and/or snapshot user features. We utilize the time series of different user metrics as temporal signals and cluster them to identify specific usage and engagement patterns for churning users (users who become inactive). This approach led to more accurate opportunity sizing for the different user personas which in turn helped prioritize interventions for customer retention. We will discuss the implementation of this approach for large-scale data, understanding the time series clusters in a human-understandable manner, and challenges associated with multi-dimensional time-series data in the presentation.
Supplemental Material
- Using Dynamic Time Warping to Find Patterns in Time Series" https://www.aaai.org/Papers/Workshops/1994/WS-94-03/WS94-03-031.pdfGoogle Scholar
- Detecting and Classifying Events in Noisy Time Series: https://journals.ametsoc.org/view/journals/atsc/71/3/jas-d-13-0182.1.xml#bib43Google Scholar
- Time-series clustering -- A decade review https://www.sciencedirect.com/science/article/abs/pii/S0306437915000733Google Scholar
- A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases https://link.springer.com/chapter/10.1007%2F3--540--45571-X_14Google Scholar
Index Terms
- Studying Long-Term User Behaviour Using Dynamic Time Warping for Customer Retention
Recommendations
Hierarchical clustering of time series data with parametric derivative dynamic time warping
Combination of DTW and DDTW applied in a method of time series clustering.Specific correction algorithm for the internal cluster validation measures.Parameter selection for any dataset, giving the best clustering performance. Dynamic Time Warping (DTW) ...
Weighted dynamic time warping for time series classification
Dynamic time warping (DTW), which finds the minimum path by providing non-linear alignments between two time series, has been widely used as a distance measure for time series classification and clustering. However, DTW does not account for the relative ...
Flexible Dynamic Time Warping for Time Series Classification
Measuring the similarity or distance between two time series sequences is critical for the classification of a set of time series sequences. Given two time series sequences, X and Y , the dynamic time warping (DTW) algorithm can calculate the distance ...
Comments