Time Series Classification Based on Temporal Features
There is currently a paper on this topic under review. And the paper is submitted to ‘Applied Soft Computing’ Journal.
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.
Code
The code for our method is in package src
Experiements
The code for experiments is in packages experiments.
Transformed data
The transformed data is in fold trans. Please unzip the rar file before using these files.
Results
The experimental results are in fold result.