# Interval Feature Transformation for Time Series Classification Using Perceptually Important Points
###### Abstract  
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 feature vector. The IFT distinguishes the best top-k discriminative feature vectors from a data set by information gain. Utilizing these discriminative feature vectors, transformation is applied to generate new k-dimensional data which are lower-dimensional representations of the original data. In order to verify the effectiveness of this method, we use the transformed data in conjunction with some traditional classifiers to solve time series classification problems and make comparative experiments to several state-of-the-art algorithms. Experiment results verify the effectiveness, noise robustness and interpretability of the IFT. interval feature; transformation; time series classification; perceptually important points 100 100 1  
classification successfully. A spate of feature-based methods has been proposed in recent years, which reframe the problem in terms of interpretable features. In general, interpretable features can be derived from either simple statistic (e.g., mean and variance) of time intervals or just a subsequence of the time series (i.e., shapelets). [PERSON] et al. [12] built a decision tree on three simple features: mean, standard deviation and slope. [PERSON] and [PERSON] [13] constructed a decision tree classifier by recursively searching for discriminatory shapelets based on data split via an information gain measure, and also calculated the distance to the branching shapelets. In recent years, some transformation methods have been proposed to solve the TSC problem. Time series transformation algorithms process series to create alternative data representations. [PERSON] et al. [14] proposed shapelet transformation which is used to convert the raw time series data based on the shapelets to a different data representation. The representation contains features corresponding to a specific shapelet and its value is the minimal distance to the time series. The distances between the shapelets and the time series are computed, and then these distances, as features, are used to fit a logistic regression. Learning time-series shapelets (LS) learn the shapelets as well as the coefficients of the logistic regression [15]. Bag-of-words approaches are frequently used in time series classification. The Bag-of-SFA Symbols (BOSS) algorithm extracts words from a time series and builds features representing frequencies of each word for each time series [16]. The BOSS model transforms a time series into an unordered set of SFA words, which is fast and very robust to noise. Symbolic aggregate approximation (SAX) is a symbolic representation that allows for dimensionality reduction, lower bounding, and transformation of streamed data [17]. SAX represents time series as a string of characters. The SAX transformation is often combined with other methods to solve the problem of time series classification. [PERSON] et al. [18] proposed an algorithm (SAXVSM) for time series classification based on SAX approximation of time series and vector space model. Studies have shown that the classifier can provide a greater level of performance improvement via creating a new classification data set independently. Such approaches can speed up run-time for the very long series and are not sensitive to noise data. In this paper, we present a new transformation method based on the interval feature. Interval features are the temporal features calculated over time series intervals [19], which can capture the temporal characteristics. We propose the interval feature vector and evaluate their benefits to classification in order to get the discriminative feature vectors. The discriminative feature vectors are used to generate transformed data.