Efficient Time Series Clustering by Minimizing Dynamic Time Warping Utilization

Time Warping Techniques in Clustering Time Series

The problem of obtaining an accurate forecast is becoming more and more significant as the production possibilities and technologies evolve. The accuracy of a forecast mainly depends on the dataset used for forecasting as well as on the methods employed. This study is devoted to the time series analysis. A set of known methods and techniques are used for analysing the time series; one of them i...

A Novel Approximation to Dynamic Time Warping allows Anytime Clustering of Massive Time Series Datasets

Given the ubiquity of time series data, the data mining community has spent significant time investigating the best time series similarity measure to use for various tasks and domains. After more than a decade of extensive efforts, there is increasing evidence that Dynamic Time Warping (DTW) is very difficult to beat. Given that, recent efforts have focused on making the intrinsically slow DTW ...

Towards more accurate clustering method by using dynamic time warping

An intrinsic problem of classifiers based on machine learning (ML) methods is that their learning time grows as the size and complexity of the training dataset increases. For this reason, it is important to have efficient computational methods and algorithms that can be applied on large datasets, such that it is still possible to complete the machine learning tasks in reasonable time. In this c...

Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series

Iterative Deepening Dynamic Time Warping for Time Series

Time series are a ubiquitous form of data occurring in virtually every scientific discipline and business application. There has been much recent work on adapting data mining algorithms to time series databases. For example, Das et al. attempt to show how association rules can be learned from time series [7]. Debregeas and Hebrail [8] demonstrate a technique for scaling up time series clusterin...