… feature fusion network architecture for time series … features and distance features of time
series data. Then, it integrates these two feature types through the adaptive feature fusion …

… In [53], a feature fusion framework is … on a feature fusion framework. This model hybrids
AICA-SVR and MICA-SVR, and utilizes CCA as the feature fusion tool to extract the union feature. …

… time series feature extraction method, which can automatically extract features from time
series images during time series … the feature fusion in the transformed training sample feature …

… -dimension of time series. This paper presents a new approach for time series classification
… three parts: short-time gap feature extraction, multi-scale local feature learning, and global …

… complement rather than replace the features extracted directly from one-… two features, this
paper proposes a time series classification method based on multi-dimensional feature fusion, …

As a crucial component in the transmission system, a planetary gearbox has a relatively
complicated structure and usually operates under complex working conditions and a severe …

… –frequency domain, which can capture different unobserved features embedded in time
series data. The following subsection describes in detail the techniques adopted in this work. …

… , existing methods only use sequence features or visualization features. Therefore, this …
fusion features (TSC-FF) of sequence features extracted from raw TS and visualization features …

… feature, called local distance features, for time series classification. The local distance features
are extracted using Dynamic Time … for distance-based time series recognition methods. …

… To obtain spatiotemporal information of different scales, we utilise deep learning technology
of hybrid feature extraction and adaptive feature fusion. Multi-scale monitoring windows for …