Time series classification based on statistical features

Abstract

This paper presents a statistical feature approach in fully convolutional time series classification (TSC), which is aimed at improving the accuracy and efficiency of TSC. This method is based on fully convolutional neural networks (FCN), and there are the following two properties: statistical features in data preprocessing and finetuning strategies in network training. The key steps are described as follows: firstly, by the window slicing principle, dividing the original time series into multiple equal-length subsequences; secondly, by extracting statistical features on each subsequence, in order to form a new sequence as the input of the neural network, and training neural network by the fine-tuning idea; thirdly, by evaluating the classification performance about test sets; and finally, by comparing the sample sequence complexity and network classification loss accuracy with the FCN using the original sequence. Our experimental results show that the proposed method improved the classification effects of FCN and the residual network (ResNet), which means that it has a generalization ability to the network structures.

A TimeImageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft Systems

“…Wang et al [22] suggested three techniques to image time series, two based on Gramian Angular Fields (GAF) and one based on Markov Transition Fields (MTF). The majority of time series problems, such as classification (e.g., Fulcher et al [23] and Lei et al [24]); time-series clustering (e.g., Aghabozorgi et al [25]; Bandara et al [26]); and anomaly detection (e.g., Hyndman et al [27]), are due to feature representations. Nonetheless, the network structure and hyper-parameters setting has a significant impact on the performance of neural networks.…”

Dynamic Multi-Scale Convolutional Neural Network for Time Series Classification

“…Methods include the bag of SFA symbols (BOSS) [7], learned shapelets (LS) [8], and learned pattern similarity (LPS) [9]. The statistical time series methods [10]- [13] employ summary statistics of time series as features for classification. For example, time series forest (TSF) [11] and the time series bag of features (TSBF) [12] use simple statistical features such as mean, standard deviation, and slop for classification.…”

“…For example, time series forest (TSF) [11] and the time series bag of features (TSBF) [12] use simple statistical features such as mean, standard deviation, and slop for classification. Lei et al [13] use the statistical features (maximum, mean, polar difference, variance, etc.) from the subsequences to constitute new time series, and combine with the fully convolutional network for time series classification.…”

A Novel Long- and Short-Term Memory Network with Time Series Data Analysis Capabilities

“…In the past 20 years, the analysis of time series data has received widespread attention, and various time series data research methods have been proposed. Related research mainly includes similar time series research [1][2][3], time series search and query [4], dimensionality reduction [5,6], segmentation [7,8], anomaly detection [9,10], topic discovery [11], prediction [12,13], clustering [14][15][16][17][18][19][20][21][22], classification [23][24][25], and segmentation [26,27].…”