classification

Time-Series Analysis: Wearable Devices using DTW and kNN

Time-Series Analysis: Wearable Devices using DTW and kNN

Recently, there has been great success in time series analyses by applying dynamic time warping (DTW). Indeed, when combined with simple algorithms such as k-Nearest Neighbours, DTW has reproduced accuracies of  state-of-the-art algorithms, such as deep neural nets etc.

We will discuss here a supervised classification example of wearable devices and a possible use-case for DTW.

 

Dynamic Time Warping: Time Series Analysis II

Dynamic Time Warping: Time Series Analysis II

Dynamic Time Warping (DTW) is an intelligent, dynamically adjusted metric that allows more flexibility when used in combination with any distance dependent algorithm. This flexibility allows for better classification results in many different time-series analyses. 

DTW allows us to retain the temporal dynamics by directly modeling the time-series. As its names suggests, the usual Euclidean distance in problems is replaced with a dynamically adjusted metric.