Abstract
Along with the widespread application of Internet of things technology, time series classification have been becoming a research hotspot in the field of data mining for massive sensing devices generate time series all the time. However, how to accurately classify time series based on intuitively interpretable features is still a huge challenge. For this, we proposed a new Time Series Classification method based on Temporal Features (TSC-TF). TSC-TF firstly generates some temporal feature candidates through time series segmentation. And then, TSC-TF selects temporal feature according the importance measures with the help of a random forest. Finally, TSC-TF trains a fully convolutional network to obtain high accuracy. Experiments on various datasets from the UCR time series classification archive demonstrate the superiority of our method. Besides, we have released the codes and parameters to facilitate the community research.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
