Interval Feature Transformation for Time Series Classification Using Perceptually Important Points
A novel feature reconstruction method, referred to as interval feature transformation (IFT), is proposed for time series classification.The IFT uses perceptually important points to segment the series dynamically into subsequences of unequal length, and then extract interval features from each time series subsequence as a Grooming feature vector.Th