ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis

Over a significant period in the past, the convolutional network [He et al., 2016; Zheng et al., 2014; Middlehurst This paper introduces ConvTimeNet, a novel deep et al., 2023] has played a crucial role in time series analysis, hierarchical fully convolutional network designed largely due to its inherent properties that strike an excellent to serve as a general-purpose model for time series balance between computational efficiency and representation analysis. The key design of this network is twofold, quality. Data back to the past years, many representative designed to overcome the limitations of traditional works [Bagnall et al., 2017] of time series analysis typically convolutional networks. Firstly, we propose an employ convolutional networks as the backbone. For adaptive segmentation of time series into sub-series instance, temporal convolutional network (TCN[Bai et al., level patches, treating these as fundamental modeling 2018]) and its variants are widely used in modeling temporal units. This setting avoids the sparsity semantics variation dependence for the time series forecasting task. Furthermore, associated with raw point-level time steps. Secondly, a large number of works (such as InceptionTime[Ismail we design a fully convolutional block by Fawaz et al., 2020], MiniRocket[Dempster et al., 2021], skillfully integrating deepwise and pointwise convolution and MCNN[Cui et al., 2016]) are also proposed by employing operations, following the advanced building convolutional networks to identify informative patterns block style employed in Transformer encoders.