Feature based learning of time series sequences contains a systematic step of preprocessing, representing and analyzing the properties of time series elements. Representational features include the mapping of time series properties namely trend, seasonality and stationarity. Usually, the segmented generation of representational structures does not contain the global features of a time series sequence which can influence the learning algorithms. Global information of each time series sequence reinforces the respective segmental properties present in it. Identifying, extracting and processing of global features which are common to all time series sequences are challenging tasks in time series feature learning. Hence, we propose a novel set of global features which provides an additional representational leverage to feature based time series learning scenarios. The feature enriched primitives can provide an additional information on the global trend pattern in each of the time series sequences. This enables the learning algorithms to process the time series sequences with the awareness of trend information. We formed a minimum number of most influential trend features which describe the behavior of time series sequences. Thus the dimensionality of the features are preserved which influence the performance of various learning algorithms. The experiments on this novel representational structures are performed on UCR-2018 time series archive which contains 128 datasets. We also represented the trend sequences in a pictorial form named positional size diagram (PSD) and aggregated all the instances of the datasets into an auxiliary data representation named positional dataset (PD). We compared six traditional classification algorithms namely k-nearest neighbor (k-NN), logistic regression (LR), support vector (SV), decision tree (DC), gaussian naive bayes (GNB) and random forest (RF) with trendlets. The additional set of global features enrich the trendlets with supplementary information about the trend of time series sequences. The classification accuracy of the aforementioned algorithms shows a significant improvement with this additional set of global features.

基于特征的时间序列序列学习包含系统的预处理，表示和分析时间序列元素属性的步骤。代表性特征包括时间序列属性的映射，即趋势，季节性和平稳性。通常，代表性结构的分段生成不包含会影响学习算法的时间序列的全局特征。每个时间序列的全局信息加强了其中存在的各个分段特性。在时间序列特征学习中，识别，提取和处理所有时间序列通用的全局特征是具有挑战性的任务。因此，我们提出了一套新颖的全局特征，它为基于特征的时间序列学习场景提供了额外的代表性。功能丰富的原语可以提供有关每个时间序列中全局趋势模式的其他信息。这使学习算法可以在趋势信息意识下处理时间序列。我们形成了数量最少的最具影响力的趋势特征，这些特征描述了时间序列的行为。因此，保留了特征的维度，这影响了各种学习算法的性能。在包含128个数据集的UCR-2018时间序列档案上执行了关于这种新颖表示结构的实验。我们还以称为位置尺寸图（PSD）的图形形式表示趋势序​​列，并将数据集的所有实例聚合到名为位置数据集（PD）的辅助数据表示中。我们比较了六种传统的分类算法，即k最近邻（k-NN），逻辑回归（LR），支持向量（SV），决策树（DC），高斯朴素贝叶斯（GNB）和随机森林（RF）与趋势波。额外的全局特征集通过有关时间序列序列趋势的补充信息丰富了趋势。前述算法的分类精度在这组额外的全局特征中显示出显着的改进。我们比较了六种传统的分类算法，即k最近邻（k-NN），逻辑回归（LR），支持向量（SV），决策树（DC），高斯朴素贝叶斯（GNB）和随机森林（RF）与趋势波。额外的全局特征集通过有关时间序列序列趋势的补充信息丰富了趋势。前述算法的分类精度在这组额外的全局特征中显示出显着的改进。我们比较了六种传统的分类算法，即k最近邻（k-NN），逻辑回归（LR），支持向量（SV），决策树（DC），高斯朴素贝叶斯（GNB）和随机森林（RF）与趋势波。额外的全局特征集通过有关时间序列序列趋势的补充信息丰富了趋势。前述算法的分类精度在这组额外的全局特征中显示出显着的改进。