Time series classification for varying length series

Noname manuscript No. (will be inserted by the editor)Time series classification for varying length series Chang Wei Tan · Fran cois Petitjean· Eamonn Keogh · Geoffrey I. Webb the date of receipt and acceptance should be inserted later Abstract Research into time series classification has tended to focus on the case of series of uniform length. However, it is common for real-world time series data to have unequal lengths. Differing time series lengths may arise from a number of fundamentally different mechanisms. In this work, we identify and evaluate two classes of such mechanisms - variations in sampling rate relative to the relevant signal and variations between the start and end points of one time series relative to one another. We investigate how time series generated by each of these classes of mechanism are best addressed for time series classification. We perform extensive experiments and provide practical recommendations on how variations in length should be handled in time series classification. Keywords Time Series Classification, Proximity Forest, Dynamic Time Warping 1 Introduction Time series classification (TSC) is an important task in many modern world applications such as remote sensing (Pelletier et al., 2019; Petitjean et al., 2012), astronomy (Batista et al., 2011), speech recognition (Hamooni et al., 2016), and insect classification (Chen et al., 2014). The time series to be classified are the observed outputs generated by some process. The classification task often relates to identifying the class of the process that generated the series. Each class of process might be considered as a realization of one or more ideals (in the Platonic sense) or prototypes. The resulting time series can then beChang Wei Tan · Fran cois Petitjean· Geoffrey I. Webb Faculty of Information Technology 25 Exhibition Walk Monash University, Melbourne VIC 3800, Australia Email: chang.tan@monash.edu,francois.petitjean@monash.edu,geoff.webb@monash.edu An observed time series might differ from the ideal in many ways. Much of the research on time series distance measures in the last decade can be seen as the introduction of techniques to mitigate these differences, either as a preprocessing step or directly in a distance measure. For example, variations in amplitude and offset are typically addressed in time series classification by normalization of the series (Rakthanmanon et al., 2012). Some observed values may be erroneous and might be addressed by outlier detection (Basu and Meckesheimer, 2007) and subsequent reinterpolation (Pelletier et al., 2019).