# Optimizing dynamic time warping’s window width for time series data mining applications

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/optimizing-dynamic-time-warping-s-window-width-for-time-series-data-mining/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Hoang Anh Dau,Diego Furtado Silva,François Petitjean,Germain Forestier,Anthony Bagnall,Abdullah Mueen,Eamonn Keogh |
| Citations | 96 |
| DOI | 10.1007/s10618-018-0565-y |
| Fields | Computer Science |
| Open Access | true |
| OA Status | green |
| OA URL | https://ueaeprints.uea.ac.uk/id/eprint/66665/1/setting_w_DMKD_v043_publication.pdf |
| OpenAlex ID | https://openalex.org/W2797178690 |
| Type | article |
| Year | 2018 |

## Paper authors

- [François Petitjean](https://scholariq.org/researchers/francois-petitjean/)

## Paper primary topic

- [Time Series Analysis and Forecasting](https://scholariq.org/topics/time-series-analysis-and-forecasting/)

## Paper topics

- [Time Series Analysis and Forecasting](https://scholariq.org/topics/time-series-analysis-and-forecasting/)
- [Music and Audio Processing](https://scholariq.org/topics/music-and-audio-processing/)
- [Anomaly Detection Techniques and Applications](https://scholariq.org/topics/anomaly-detection-techniques-and-applications/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.