Time Series Similarity Search Methods for Sensor Data

Time series is a type of dynamic data used in many applications. Time series speed may vary from milliseconds to years or decades. In past decade, rise in various sensor based technologies have made time series sensor data available easily and in larger extent. Therefore, high dimensionality of the data in customized applications is always a challenging task for efficient mathematical computing accuracy and performance optimization. One of the major operations performed on time series is finding out similarity between two or more time series. Two time series can be considered similar on the basis of distance between them. Computation of these distances is achieved by various methods. This research study aims to compare eight such methods for accelerometer sensor data collected from smartphone based accelerometer during car and scooter ride. This study also proposes a modified method of distance computation considering tyre pressure and weight of the vehicle. Research findings have shown that modified method of DTW (dynamic time warping) is proved more efficient in distinguishing time series generated by two different weights' vehicles. Results have shown as maximum of 67% recognition rate is achieved by modified DTW method compared to traditional DTW method.