Time Series Classification Accuracy Research Articles

Slope Entropy Characterisation: An Asymmetric Approach to Threshold Parameters Role Analysis.

Slope Entropy (SlpEn) is a novel method recently proposed in the field of time series entropy estimation. In addition to the well-known embedded dimension parameter, m, used in other methods, it applies two additional thresholds, denoted as δ and γ, to derive a symbolic representation of a data subsequence. The original paper introducing SlpEn provided some guidelines for recommended specific values of these two parameters, which have been successfully followed in subsequent studies. However, a deeper understanding of the role of these thresholds is necessary to explore the potential for further SlpEn optimisations. Some works have already addressed the role of δ, but in this paper, we extend this investigation to include the role of γ and explore the impact of using an asymmetric scheme to select threshold values. We conduct a comparative analysis between the standard SlpEn method as initially proposed and an optimised version obtained through a grid search to maximise signal classification performance based on SlpEn. The results confirm that the optimised version achieves higher time series classification accuracy, albeit at the cost of significantly increased computational complexity.

Slope Entropy Characterisation: The Role of the δ Parameter

Many time series entropy calculation methods have been proposed in the last few years. They are mainly used as numerical features for signal classification in any scientific field where data series are involved. We recently proposed a new method, Slope Entropy (SlpEn), based on the relative frequency of differences between consecutive samples of a time series, thresholded using two input parameters, and . In principle, was proposed to account for differences in the vicinity of the 0 region (namely, ties) and, therefore, was usually set at small values such as 0.001. However, there is no study that really quantifies the role of this parameter using this default or other configurations, despite the good SlpEn results so far. The present paper addresses this issue, removing from the SlpEn calculation to assess its real influence on classification performance, or optimising its value by means of a grid search in order to find out if other values beyond the 0.001 value provide significant time series classification accuracy gains. Although the inclusion of this parameter does improve classification accuracy according to experimental results, gains of at most probably do not support the additional effort required. Therefore, SlpEn simplification could be seen as a real alternative.

Adaptive feature fusion for time series classification

Time series classification is one of the most critical and challenging problems in data mining, which exists widely in various fields and has essential research significance. However, to improve the accuracy of time series classification is still a challenging task. In this paper, we propose an Adaptive Feature Fusion Network (AFFNet) to enhance the accuracy of time series classification. The network can adaptively fuse multi-scale temporal features and distance features of time series for classification. Specifically, the main work of this paper includes three aspects: firstly, we propose a multi-scale dynamic convolutional network to extract multi-scale temporal features of time series. Thus, it retains the high efficiency of dynamic convolution and can extract multi-scale data features. Secondly, we present a distance prototype network to extract the distance features of time series. This network obtains the distance features by calculating the distance between the prototype and embedding. Finally, we construct an adaptive feature fusion module to effectively fuse multi-scale temporal and distance features, solving the problem that two features with different semantics cannot be effectively fused. Experimental results on a large number of UCR datasets indicate that our AFFNet achieves higher accuracies than state-of-the-art models on most datasets, as well as on the WISDM, HAR and Opportunity datasets, demonstrating its effectiveness.

Regularized shapelet learning for scalable time series classification

Time series shapelets are subsequences that best split time series data into classes. Therefore, shapelet discovery has attracted considerable interest in the time series classification community. However, almost all state-of-the-art shapelet-based time series classification methods have an inevitably high computational cost. To overcome this drawback, we present a regularized shapelet learning framework in which the fused lasso regularizer is used to maintain the time order of the shapelets and different loss functions can be employed to improve the speed and accuracy of the time series classification. The proposed framework converts the traditional brute force shapelet searching process into a regularized machine learning problem. The most prominent advantage of this conversion is that the speed of the shapelet learning process and the discrimination of the learned shapelets are both theoretically guaranteed. As such, both the speed and accuracy of the shapelet-based time series classification are improved in this paper. Comparison experiments on several datasets show that our framework effectively reduces the training time of time series classification while improving the classification accuracy.

A time-series classification approach based on change detection for rapid land cover mapping

Land-Use/Land-Cover Time-Series Classification (LULC-TSC) is an important and challenging problem in terrestrial remote sensing. Detecting change-points, dividing the entire time series into multiple invariant subsequences, and classifying the subsequences can improve LULC classification efficiency. Therefore, we have proposed a Time-Series Classification approach based on Change Detection (TSCCD) for rapid LULC mapping that uses the Prophet algorithm to detect the ground-cover change-points and perform time-series segmentation in a time dimension and the DTW (Dynamic Time Warping) algorithm to classify the sub-time series. Since we can assume that the ground cover remains unchanged in each subsequence, only one time-training sample selection and one LULC classification are needed, which greatly improves the work efficiency. Prophet can accurately detect large and subtle changes, capture change direction and change rate, and is strongly robust for handling noise and missing data. DTW is mainly used to improve the accuracy of time-series classification and to resolve the time misalignment problems of ground-cover series data caused by irregular observations or missing values. The results of comparative experiments with BFAST, LandTrendR, and CCDC using simulated time-series showed that TSCCD can detect large and subtle changes and capture change direction and change rate, performing substantially better than the other three contrasting algorithms overall in time-series change detection. Finally, the MODIS (Moderate Resolution Imaging Spectroradiometer) time-series images of Wuhan City from 2000 to 2018 were selected for TSCCD, and the results of China’s national land-use surveys in 2000, 2005, 2008, 2010, 2013, and 2015 were used for cross-validation. The results showed that the classification accuracy of each tested subsequence was higher than 90% and that most Kappa coefficients were greater than 0.9. This means that the proposed TSCCD approach can effectively solve real LULC-TSC problems and has high application value. It can be used for large-area, long time-series LULC classification, which is of great guiding significance for studying global environmental changes, forest-cover changes, and conducting land-use surveys.

Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles

Recently, two ideas have been explored that lead to more accurate algorithms for time-series classification (TSC). First, it has been shown that the simplest way to gain improvement on TSC problems is to transform into an alternative data space where discriminatory features are more easily detected. Second, it was demonstrated that with a single data representation, improved accuracy can be achieved through simple ensemble schemes. We combine these two principles to test the hypothesis that forming a collective of ensembles of classifiers on different data transformations improves the accuracy of time-series classification. The collective contains classifiers constructed in the time, frequency, change, and shapelet transformation domains. For the time domain, we use a set of elastic distance measures. For the other domains, we use a range of standard classifiers. Through extensive experimentation on 72 datasets, including all of the 46 UCR datasets, we demonstrate that the simple collective formed by including all classifiers in one ensemble is significantly more accurate than any of its components and any other previously published TSC algorithm. We investigate alternative hierarchical collective structures and demonstrate the utility of the approach on a new problem involving classifying Caenorhabditis elegans mutant types.

Piecewise aggregate approximation based on the major shape features of time series

Piecewise aggregate approximation (PAA) is one of the most widely used time series representation methods in which subsequences of the time series are replaced with their mean values. Extensions of the PAA technique are often used in time series data mining. In this paper, we propose a new piecewise aggregate approximation method based on the major shape features of the time series and a novel distance measure. The major shape features, including the average (mean), slope, curvature and rate of change of the curvature, are used to describe the characteristics of each subsequence of the time series. The distance function used to measure the similarity between two time series satisfies a lower bound on the Euclidean distance, which guarantees that false dismissal is avoided when the distance measure is used in time series indexing and similarity searching. Through several experiments, we demonstrate that the proposed approach not only provides a good fit to the time series and improves the tightness of the lower bound but also achieves improved time series classification accuracy with a low dimensionality.